Category: 8. Health

Introduction

Tetralogy of Fallot (ToF) is a congenital cyanotic heart defect, first described in detail by Étienne-Louis Fallot in 1888. It is characterized by a combination of four distinct anatomical abnormalities: right ventricular (RV) hypertrophy, a ventricular septal defect (VSD), obstruction of the right ventricular outflow tract (RVOT), and an overriding aorta. These structural anomalies result in altered hemodynamics, reducing pulmonary blood flow and systemic cyanosis.¹ Early primary repair of Tetralogy of Fallot (ToF) has been advocated since the 1970s and is now routinely performed with excellent outcomes.² This approach has become the standard of care, as it promptly addresses the anatomical abnormalities and associated hemodynamic consequences, reducing long-term morbidity and mortality. However, despite the widespread adoption of early surgical intervention, occasional cases of patients who have not undergone repair survive into adulthood.^3,4 However, delaying the diagnosis and late intervention are highly associated with poor outcomes.⁵ According to large observational series, 24% of individuals with an uncorrected TOF die before the age of 10, and only 4% survive beyond their thirties.^6,7. We present a unique case of an individual with uncorrected Tetralogy of Fallot (TOF) who has survived into adulthood with a relatively preserved quality of life despite the absence of surgical intervention. The lack of corrective surgery was primarily due to financial constraints and limited access to specialized cardiovascular care. The patient resides in Somalia, a low-income country where healthcare infrastructure remains underdeveloped, and access to advanced medical and surgical interventions is severely restricted. This case underscores the significant challenges faced by individuals with congenital heart disease (CHD) in resource-limited settings. It highlights the urgent need for improved access to pediatric and adult congenital cardiac care in such environments.

Case Presentation

A 43-year-old male presented with a history of recurrent syncope, reporting three episodes over the past six months. The most recent episode occurred at approximately 3:00 AM, during which the patient was found lying unresponsive on his bed by his family and was subsequently rushed to the hospital. The patient has a longstanding history of exertional dyspnea dating back to childhood, which was particularly pronounced during activities such as climbing hills. Due to his family’s nomadic lifestyle, he was often transported on camels during travels to accommodate his physical limitations. Additionally, the patient reported episodes of epistaxis and hemoptysis, further complicating his clinical picture.

The patient’s past medical history includes a diagnosis of congenital heart disease of unknown type in 1997. Despite being referred to a cardiac center in Djibouti, a neighboring country, for further management, financial constraints prevented him from accessing specialized care. Over the years, the patient has experienced progressive exertional dyspnea, which has significantly impacted his quality of life and occupational capacity. Initially, he worked in charcoal production and later as a painter, but he was forced to retire due to worsening symptoms. He is the father of six children and has been the primary provider for his family.

Physical Examination

General Appearance

The patient appeared to be in no acute distress and exhibited a normal overall appearance without signs of cachexia.

Vital Signs Were

Blood pressure 110/60 mmHg, heart rate 80 beats/minute, respiratory rate 18 breaths/minute, temperature 35.8 °C, and oxygen saturation 85% on room air.

General Examination

• Clubbing: Grade 3 clubbing was observed see Figure 5.
• Cyanosis: No cyanosis was noted.
• Edema: No peripheral edema was present.

Cardiovascular System (CVS)

• S1 and s2 are audible with a pansystolic murmur, graded 3/6 in intensity, was auscultated. The murmur was most prominent in the tricuspid and mitral areas but was not associated with a palpable thrill.

Abdomen

• The abdomen was soft and non-tender on palpation.
• No abdominal swelling or organomegaly was detected.

Peripheral Examination

• No ankle edema or other signs of peripheral vascular compromise were observed.

Nervous Examination

• A brief neurological assessment showed the patient to be alert and fully oriented to person, place, and time. There were no obvious motor or sensory impairments. Examination of cranial nerves II through XII revealed no gross abnormalities. Additionally, there were no clinical signs suggestive of meningeal irritation.

Investigations

Complete Blood Count (CBC)

• Polycythemia: Hemoglobin (Hb) level was elevated at 21 mg/dL, indicative of secondary polycythemia, likely due to chronic hypoxemia associated with congenital heart disease.
• Thrombocytopenia: A Low platelet count was noted, possibly related to chronic disease or other underlying factors.

Echocardiographic evaluation of the patient revealed a large malalignment subaortic ventricular septal defect (VSD)with an overriding aorta of less than 50%, indicative of abnormal conal septal development. Right ventricular hypertrophy (RVH) was observed, consistent with chronic pressure overload. Infundibular pulmonary stenosis was noted, contributing to right ventricular outflow obstruction. Additionally, an interatrial septal (IAS) aneurysm was identified, suggesting a structural abnormality that may have clinical significance (Figures 1 and 2).

Figure 1 Is a long-axis view demonstrating the VSD with color Doppler imaging, showing flow across the defect penetrating the interventricular septum.

Figure 2 Is an apical four-chamber view providing additional visualization of the VSD.

Assessment

• Tetralogy of Fallot (ToF): Known congenital heart defect with concerns for arrhythmias due to recent syncope.
• Possible Old Inferior Myocardial Infarction (MI): ECG shows pathological Q waves in inferior leads, possibly due to chronic hypoxemia or coronary artery disease.
• Secondary Polycythemia: Elevated hemoglobin (21 mg/dL) as a compensatory response to chronic hypoxemia, increasing thromboembolic risk.
• Thrombocytopenia: Low platelet count, possibly secondary to chronic disease or polycythemia.

Plan

• Medical Management:

  • Aspirin (81 mg daily): To reduce thromboembolic risk from severe polycythemia and possible atherosclerosis indicated by ECG.
  • Atorvastatin (20 mg daily): Secondary prevention for suspected old inferior MI despite unknown lipid levels.
  • Hydroxyurea (500 mg daily): To control symptomatic secondary polycythemia by lowering erythropoiesis and blood viscosity.
  • Home Oxygen Therapy (as needed): To relieve chronic hypoxemia symptoms, especially exertional dyspnea.

• Phlebotomy: Weekly 250 mL blood transfusion to manage polycythemia, with caution due to thrombocytopenia.
• Further Investigation: Holter monitoring for arrhythmias.
• Referral for Cardiac Surgery: Consideration for pulmonary valve replacement or complete repair.
• Lifestyle & Follow-up: Avoid strenuous activity, and regularly monitor hemoglobin, platelets, and cardiac function.

Electrocardiogram (ECG)

• Rate: 75 beats per minute.
• Rhythm: Sinus rhythm.
• Axis: Extreme right axis deviation.
• P Wave: P-pulmonale (tall, peaked P waves in leads II, III, and aVF) suggests right atrial enlargement.
• Q Wave: Pathological Q waves were observed in the inferior leads (II, III, and aVF), suggesting a possible old inferior wall myocardial infarction.
• R Wave:

  1. Prominent R wave in V1 and aVR.
  2. Deep S wave in V5 and V6.
  3. Poor R-wave progression across the precordial leads (Figure 3).
     

     Figure 3 ECG showing right ventricular hypertrophy, right atrial enlargement (P-pulmonale), and pathological Q waves in inferior leads suggest an old inferior wall myocardial infarction.

• Findings:

  • Right Ventricular Hypertrophy (RVH): Suggested by the prominent R wave in V1 and deep S wave in V5/V6.
  • P-pulmonale: Indicative of right atrial enlargement, likely secondary to pulmonary hypertension or chronic right heart strain.
  • Old Infarction: Pathological Q waves in the inferior leads raise the possibility of a prior inferior wall myocardial infarction.

Chest X-Ray

• The chest X-ray reveals characteristics of Tetralogy of Fallot, notably a boot-shaped heart caused by enlargement of the right ventricle (Figure 4).
  

  Figure 4 Chest X-ray showing features of tetralogy of Fallot, including a boot-shaped heart due to right ventricular hypertrophy.

Physical Appearance

• Hand of the patient showing evidence of digital clubbing (Figure 5).
  

  Figure 5 Hand of the patient showing evidence of digital clubbing.

Discussion

Tetralogy of Fallot (TOF) is a congenital cardiac anomaly characterized by a ventricular septal defect, right ventricular outflow tract obstruction, overriding of the aortic root, and right ventricular hypertrophy. It occurs in approximately 3 per 10000 live births, accounting for 7–10% of all congenital heart defects. Clinical presentation typically occurs in the neonatal period, with cyanosis varying in severity based on the degree of right ventricular outflow obstruction. The etiology is multifactorial, with genetic and environmental factors playing a role. Maternal conditions such as diabetes and phenylketonuria, as well as chromosomal anomalies like trisomies 21, 18, and 13, have been associated with TOF, though recent evidence suggests a stronger link with 22q11.2 microdeletion. The recurrence risk in affected families is approximately 3%.⁸

In diagnosing congenital heart diseases like Tetralogy of Fallot (ToF), readily available tools such as electrocardiography (ECG) and echocardiography are indispensable, offering a non-invasive approach. An ECG can reveal right axis deviation and right ventricular hypertrophy, indicative of the strain on the heart caused by ToF. Furthermore, echocardiography (2D ECHO) is crucial for confirming the diagnosis of ToF, as it can visualize the key anatomical defects, including ventricular septal defect (VSD), overriding aorta, and right ventricular outflow tract (RVOT) obstruction.⁹

While ECG and standard echocardiography are essential for the initial diagnosis and monitoring of Tetralogy of Fallot (ToF), particularly in resource-limited settings due to their accessibility, advanced imaging modalities are often required for comprehensive anatomical and hemodynamic evaluation. Cardiac MRI (cMRI) is central in assessing ventricular function, pulmonary valve pathology, and right ventricular outflow tract (RVOT) morphology. Emerging techniques such as 4D-flow MRI enhance diagnostic precision by enabling dynamic flow analysis, offering insights into adverse hemodynamic patterns, and assisting with risk stratification and intervention planning.¹⁰ While cardiac catheterization remains the gold standard for direct hemodynamic measurements and detailed visualization of pulmonary arteries or complex vascular anomalies like Major Aortopulmonary Collateral Arteries (MAPCAs), its use is often limited in low-resource environments. Therefore, ECG and echocardiography remain indispensable tools for initial evaluation and follow-up in such settings.⁹ In our patient, ECG and echocardiography were indispensable for diagnosing Tof.

Tetralogy of Fallot (TOF) is a cyanotic congenital heart disease associated with high mortality rates among unrepaired patients. Survival beyond 20 years is limited to approximately 10%, with only 3% reaching 40. In contrast, over 90% of patients undergoing surgical repair survive into adulthood. In developed countries, most individuals with TOF receive timely surgical intervention, which alleviates right ventricular outflow tract (RVOT) obstruction and significantly reduces mortality. However, in low-resource settings, access to surgical repair remains limited, particularly among patients from low socioeconomic backgrounds. This disparity contributes to poorer long-term outcomes and increased mortality in these populations.¹¹

In some cases, patients may not receive an accurate diagnosis during childhood, as was observed in our case. The first clinical suspicion of Tetralogy of Fallot (TOF) only arose when the patient reached adulthood. Confirming the diagnosis required advanced imaging studies, which were often financially inaccessible and frequently unavailable in his setting, both during initial assessment and routine follow-up. The lack of continuous monitoring and access to diagnostic tools contributed significantly to the delayed diagnosis and management of his condition. This case highlights the critical importance of making bedside echocardiography available, even in resource-limited environments. Point-of-care ultrasound can enable earlier detection and timely intervention in congenital heart diseases, ultimately improving patient outcomes.

In the electrocardiogram (ECG) of our patient, the presence of QS waves in the inferior leads raised suspicion of an inferior myocardial infarction (MI). The relationship between myocardial infarction and Tetralogy of Fallot (TOF) is reported in the literature. A case from Turkey described a patient with TOF who experienced an MI, providing valuable insights into this uncommon association. In that case, the absence of resting cyanosis suggested a minor right-to-left shunt, potentially explaining the patient’s prolonged survival. However, following the myocardial infarction, both ventricles rapidly deteriorated, leading to severe heart failure and early mortality.¹².

The patient, a 43-year-old man, was admitted with chest pain, dyspnea, and diaphoresis. His medical history revealed dyspnea, exertional cyanosis, and palpitations since childhood. He had been diagnosed with TOF 13 years earlier, at which time cardiac catheterization was performed. However, he declined TOF corrective surgery, which may have contributed to his later cardiovascular complications.¹² This case underscores the complex interplay between congenital heart defects and ischemic heart disease, highlighting the need for careful cardiovascular risk assessment in patients with uncorrected TOF. In the literature, several cases also describe similar situations, where the coexistence of TOF and MI significantly worsens the prognosis.¹³

In our patient, syncope raised suspicion of an underlying arrhythmia. Syncope is a concerning symptom in individuals with repaired Tetralogy of Fallot (TOF). It is often associated with arrhythmias and conduction abnormalities, common long-term sequelae of surgical repair. While advancements in surgical techniques have significantly improved survival rates, residual cardiac abnormalities, scarring from patch material, atriotomy, and ventriculotomy contribute to the development of rhythm disturbances.¹⁴ However, cases of arrhythmias have also been reported in patients with unrepaired TOF.¹⁵ Similar pathophysiological mechanisms could potentially account for the syncopal episode observed in our patient.

Ideally, ambulatory ECG monitoring—such as a Holter monitor—would have been indicated to assess for transient arrhythmias that might not be captured on routine ECG. Contemporary diagnostic strategies often rely on a spectrum of prolonged monitoring modalities, including traditional 24–48-hour Holter monitors, external loop recorders (ELRs), wearable patch devices, and implantable cardiac monitors (ICMs) for prolonged surveillance in select patients. These tools are critical for establishing symptom–rhythm correlation, especially when symptoms are infrequent or unpredictable.¹⁶ As Carrington et al (2022) emphasize, the choice of monitoring modality is primarily dictated by the frequency and nature of symptoms, with longer-duration or patient-activated monitors being particularly advantageous for episodic events. Unfortunately, such diagnostic tools were unavailable due to limitations inherent in our resource-constrained setting. Consequently, only a standard one-minute 12-lead ECG was performed, which showed no evidence of acute ischemia or arrhythmic disturbances at the recording time.

Conclusion

Tetralogy of Fallot (TOF) is a congenital heart defect with significant long-term risks, particularly in uncorrected cases. Our patient’s ECG suggested an inferior myocardial infarction, while syncope raised suspicion of arrhythmias, though Holter monitoring was unavailable. This case underscores the critical need for early diagnosis, timely surgical intervention, and continuous cardiac surveillance. In resource-limited settings, improving access to echocardiography and ambulatory ECG monitoring is essential for mitigating complications and improving outcomes in patients with uncorrected TOF.

Authors’ Information

• Dr Abdirahman A Warfaa: Cardiology Specialist at Darussalam Health Care and Cigaal Interventional Cardiology Center; also teaches at Amoud University.• Dr. Abdirahman Ibrahim Said: Internal Medicine Specialist at Borama Regional Hospital and Alaaleh Hospital; Clinical Coordinator for undergraduate programs at Amoud University College of Health Sciences.• Dr. Mohamoud Abdulahi: Orthopedics Specialist at Al-Hayat Hospital, Dar es Salaam Polyclinic, and Alaaleh Hospital; Dean of the School of Medicine, Amoud University.• Mohamed Said Hassan: Public Health Researcher; Head of the Medical School Research Committee.

Manuscript Submission

We confirm that this manuscript is original and has not been submitted elsewhere for publication.

Ethical Approval

The Ethical Committee of Amoud University granted ethical approval for this study, including permission for publication (Reference: 0100-AU-REC-2025).

Consent for Publication

The patient provided written informed consent after receiving a detailed explanation of the study’s purpose, procedures, and potential for publication. This consent included permission to publish all clinical details and associated images in this report. Patient anonymity has been preserved.

Acknowledgments

We extend our gratitude to the healthcare team at Darussalam Health Care for the care provided to the patient and the follow-up they gave.

Author Contributions

All authors contributed significantly to developing this case report, including the conception and interpretation of clinical findings. They participated in drafting, revising, or critically reviewing the manuscript; approved the final version to be published; agreed on the journal to which the case report was submitted; and took full responsibility for all aspects of the work.

Funding

This research received no financial support from external sources.

Disclosure

The authors declare that there are no conflicts of interest regarding the content or publication of this manuscript.

References

1. Boyer R, Kim HJ, Krishnan R. Management of unoperated tetralogy of Fallot in a 59-year-old patient. J Investig Med High Impact Case Reports. 2020;8. doi:10.1177/2324709620926908

2. Van Arsdell GS, Maharaj GS, Tom J, et al. What is the optimal age for repair of tetralogy of Fallot? Circulation. 2000;102(19). doi:10.1161/circ.102.suppl_3.iii-123

3. Dockery D. 1993. The New England journal of medicine was downloaded from nejm.org at Uniwersytet Jagiellonski Collegium Medicum on February 9, 2012. For personal use only. No other uses without permission. Copyright © 1993 Massachusetts medical society. All rights reserved. New Engl.

4. Bertranou EG, Blackstone EH, Hazelrig JB, Turner ME, Kirklin JW. Life expectancy without surgery in tetralogy of Fallot. Am J Cardiol. 1978;42(3):458–466. doi:10.1016/0002-9149(78)90941-4

5. Bernier PL, Stefanescu A, Samoukovic G, Tchervenkov CI. The challenge of congenital heart disease worldwide: epidemiologic and demographic facts. Semin Thorac Cardiovasc Surg Pediatr Card Surg Ann. 2010;13(1):26–34. doi:10.1053/j.pcsu.2010.02.005

6. Campbell M. Natural history of cyanotic malformations and comparison of all common cardiac malformations. Br Heart J. 1972;34(1):3–8. doi:10.1136/hrt.34.1.3

7. Roman S, Castellarin M. A uniquely compensated boot-shaped heart: a case of unrepaired tetralogy of Fallot with delayed symptom onset in adulthood. Chest. 2020;158(4):A280. doi:10.1016/j.chest.2020.08.281

8. Bailliard F, Anderson RH. Tetralogy of Fallot. Orphanet J Rare Dis. 2009;4(1). doi:10.1186/1750-1172-4-2

9. Kaur KS, Gupta ML, Rajput HS, Sajan C. Tetralogy of Fallot in adult – uncorrected and rare presentation: a case report. J Young Pharm. 2023;15(1):189–192. doi:10.5530/097515050444

10. Schäfer M, Mawad W. Advanced imaging technologies for assessing tetralogy of Fallot: insights into flow dynamics. CJC Pediatr Congenit Hear Dis. 2023;2(6):380–392. doi:10.1016/j.cjcpc.2023.09.011

11. Bhattarai P, Karki M, Purewal JK, Devarakonda Kumar. Unrepaired tetralogy of Fallot: a tale of delayed presentation and limited access to care. Chest. 2023;164(4):A386–A387. doi:10.1016/j.chest.2023.07.316

12. Kudat H, Ahmet BS, Vakur A, Ozcan M. A case of Fallot tetralogy admitted for acute myocardial. Case Rep. 2020;4(1):4–5.

13. Shteerman E, Singh V, Nero T, Lee M, Wilentz J, Menon V. Acute myocardial infarction in uncorrected tetralogy of Fallot. Circulation. 2002;106(4):1–2. doi:10.1161/01.cir.0000023883.39017.f4

14. Ghazaryan N, Adamyan M, Khachatryan L, Hovakimyan T. Syncope in a pregnant woman with repaired Tetralogy of Fallot: a case report. Eur Heart J Case Reports. 2022;6(6):1–5. doi:10.1093/ehjcr/ytac209

15. Gorla R, Macchi A, Franzoni I, et al. Unrepaired tetralogy of Fallot in an 85-year-old man. Congenit Heart Dis. 2012;7(5):1–4. doi:10.1111/j.1747-0803.2012.00642.x

16. Carrington M, Providência R, Chahal AAC, et al. Monitoring and diagnosing intermittent arrhythmias: evidence-based guidance and role of novel monitoring strategies. Eur Heart J Open. 2022;2(6):1–10. doi:10.1093/ehjopen/oeac072

Spending time outside can boost your mood, promote better sleep and support your immune system (plus, it’s free!). The only drawback is that outdoor time also exposes you to the sun’s skin-damaging UV rays. Over time, that could set the stage for skin cancer, the most commonly diagnosed cancer in the United States. “By far, the top risk factor for developing skin cancer is unprotected UV exposure, followed by genetic predisposition,” says dermatologist Geeta Yadav, M.D. 

There is good news, though. According to the Centers for Disease Control and Prevention,  many cases of skin cancer are largely preventable. Adopting safe sun habits like applying a broad-spectrum sunscreen, wearing a hat, sunglasses and clothes that cover your arms and legs, and staying in the shade can all lower your UV exposure and significantly reduce your risk. So can avoiding tanning beds, which also emit large amounts of UV light. 

You can also bolster your skin’s defenses from the inside out by eating more antioxidants. While diet plays a smaller role in skin cancer prevention, research reveals that antioxidants can provide additional protection to safeguard your skin from this all-too-common cancer.

How Antioxidants May Protect Against Skin Cancer

Skin cancer occurs when abnormal skin cells develop in the skin’s outermost layer, called the epidermis. What causes those abnormal cells to develop and grow? The most common cause is DNA damage from exposure to UV rays, either from the sun or tanning beds. However, there are other risk factors too, like getting older or having a family history of skin cancer. You may also be more likely to develop skin cancer if you have blue or green eyes, red or blond hair, or have skin that’s fair or burns or freckles easily. 

Of course, most of these risk factors are beyond your control. But there is one helpful step you can take, and that’s eating an antioxidant-rich diet. In fact, research has found that dietary antioxidants can help counteract some of the damage caused by UV exposure before it turns into cancer. And the list is long: selenium, zinc, copper, carotenoids, polyphenols and vitamins A, C and E may all be protective, according to research. 

They Combat Oxidative Stress

Exposure to UV light sets off a chain reaction that creates a storm of skin-damaging compounds called free radicals. That’s where antioxidants step in. “Antioxidants combat free radicals, unstable molecules that can damage cells and their DNA, proteins and lipids,” says Yadav. “When there are too many free radicals in the body to the point that antioxidants cannot help neutralize them, oxidative stress occurs, leading to cellular dysfunction. This dysfunction could manifest as early signs of aging, but it could also manifest as cancer.” Regularly consuming antioxidant-rich foods equips your body with the defenders needed to neutralize those free radicals before they cause long-term harm. 

They May Prevent the Spread of Cancerous Cells

Not all DNA damage leads to cancer. In fact, our bodies have a natural defense mechanism to kill off DNA-damaged cells before they turn cancerous and start to spread. However, it’s not foolproof, and some damage can fall through the cracks. Fortunately, research reveals that antioxidants called anthocyanins may help speed the process. While anthocyanins are found in lots of fruits and vegetables, one of the best sources for skin protection is berries. So, load up on these juicy fruits for an extra dose of prevention. 

They Help Boost Internal Sun Protection

Sunburns aren’t just painful. This inflammatory reaction in your skin can cause long-lasting damage.  Enter antioxidant-rich foods. Research has found that they help absorb some of the sun’s harmful UV rays and reduce inflammation to decrease the development of sunburn., For instance, one study found that carotenoids, antioxidants found in yellow, orange and red fruits and vegetables, could provide the equivalent sun protection to SPF 4 sunscreen. For the biggest bang, think tomatoes. They’re filled with a carotenoid called lycopene that’s been shown to guard against sun damage from the inside out. 

Tips to Enjoy More Antioxidants

If you’re gearing up to spend more time outdoors, these tips can help you provide your skin with an extra layer of antioxidant protection. 

• Eat the Rainbow: An easy rule of thumb for adding more antioxidants to your diet is to add more color to your plate. Fruits and vegetables with bright, deep hues are often the richest source of these beneficial compounds. 
• Brew a Cup of Green Tea: There’s a reason green tea is added to face creams, masks and serums. It’s rich in antioxidants called catechins that have been shown to calm UV-related skin inflammation. 
• Savor Some Dark Chocolate: While chocolate may not prevent skin cancer, it contains inflammation-taming antioxidants called polyphenols that may improve skin hydration and circulation. Since dark chocolate contains the most polyphenols, the darker the chocolate, the better!

Our Expert Take

Getting regular skin checks and practicing safe sun habits like applying sunscreen, wearing a hat and protective clothing, and staying in the shade may all help reduce your risk of skin cancer. While diet plays a much smaller role, research has found that antioxidants may offer additional protection. Antioxidants are believed to combat cancer-causing oxidative stress, slow the spread of cancer cells and boost your body’s internal defenses against inflammation and sunburn. And the best way to get more of them isn’t a pill or powder. It’s a diet rich in colorful fruits and vegetables. So, before you hit the beach, park or pool, head to the produce aisle!

Introduction

Nonalcoholic fatty liver disease (NAFLD) is a widespread chronic liver condition, affecting an estimated global prevalence of 37.8%, which has significantly increased from 25.5% around 2005.¹ The terminology for this condition has evolved to metabolic dysfunction-associated steatotic liver disease (MASLD), which more accurately reflects its metabolic basis.² However, we continue to use NAFLD in this manuscript for consistency with historical GWAS datasets. NAFLD encompasses a spectrum of liver disorders, ranging from simple steatosis to nonalcoholic steatohepatitis (NASH), which can potentially progress to advanced stages like fibrosis, cirrhosis, and hepatocellular carcinoma.³ The disease is linked to a high risk of liver-related morbidity and metabolic syndromes, imposing a substantial burden on healthcare systems.³ Despite advancements in its treatments, the exact causes of NAFLD are not completely understood and are likely influenced by a complex interplay of genetic and environmental factors, such as lifestyle choices, dietary habits, and exposure to certain medications or toxins.⁴

Cell senescence is a state of irreversible cell-cycle arrest that occurs in response to various stressors, such as DNA damage, oxidative stress, and telomere shortening.⁵ It is characterized by a distinct secretory phenotype known as the senescence-associated secretory phenotype (SASP), which involves the secretion of pro-inflammatory cytokines, chemokines, and matrix metalloproteinases.⁶ In the context of NAFLD, cellular senescence is thought to play a role in the transition from simple steatosis to NASH, and potentially to more advanced stages such as fibrosis and cirrhosis.⁷ The SASP can create a pro-inflammatory and profibrotic microenvironment, which may contribute to the progression of liver disease.⁸ Additionally, senescent hepatocytes and hepatic stellate cells may directly influence the development of liver cancer through the secretion of factors that promote cell proliferation and invasion.^9,10 However, whether senescence is a marker or a potential mediator of NAFLD progression remains unclear. Therefore, a comprehensive analysis of senescence-related genes in NAFLD using a robust method is necessary to determine whether senescence is a cause or consequence of NAFLD.

Mendelian randomization (MR) offers an alternative to conduct causality assumptions that cannot be readily obtained from conventional observational studies.¹¹ By utilizing randomly allocated genetic variants as instrumental variables (IVs), MR investigates the causal connections between two factors, thereby mitigating confounding bias and reverse causality.^12,13 Summary-data-based Mendelian randomization (SMR) utilizes independent genome-wide association study (GWAS) summary statistics and quantitative trait locus (QTL) data to identify causal genes from GWAS results.¹⁴ Unlike traditional MR analysis, SMR combines multi-omics data including genetic, epigenetic, proteomic evidence to improve the accuracy and reliability of causal inference. Using this approach, potential causal associations between senescence-related genes and NAFLD were identified, followed by a heterogeneity in independent instruments (HEIDI) test.¹⁵

Here, an SMR analysis was executed to investigate the potential associations of senescence-related genes methylation, expression, and protein abundance with the risk of NAFLD.

Methods

Study Design

Figure 1 summarized the overall study design. The current SMR analysis was based on publicly available datasets obtained from previous studies and the FinnGen. In this study, IVs for senescence-related genes extracted at the methylation, gene expression and protein abundance levels. Subsequent SMR analysis was conducted for NAFLD, NASH or liver cirrhosis at these levels. To strengthen the causal inference, colocalization analysis was conducted. Through the integration of results obtained from SMR analysis at these levels, we identified causal candidate genes or proteins. The reporting of MR analysis adhered to the Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization (STROBE-MR) guidelines.¹⁶

Figure 1 Overall study design of the MR analysis. A flow chart depicts how the SMR analysis was conducted in this study.

Data Sources

GWAS summary statistics for NAFLD was obtained from publicly available databases. The primary discovery dataset (GCST90275041), which comprised 6,623 cases and 26,318 controls of the European ancestry,¹⁷ was supplemented with validation from three independent cohorts: NAFLD (2,568 cases and 409,613 controls) and NASH (175 cases and 412,006 controls) cohorts from FinnGen, and cohort of liver cirrhosis in NAFLD (1,106 cases and 8,571 controls).¹⁸ The definition of diseases is based on the International Classification of Diseases, 9th and 10th Revision (ICD-9 and ICD-10).The detailed information for each phenotypic outcome data was provided in Supplementary Table 1. There is no overlap in samples between the discovery and validation cohorts. This study utilized summary statistics from public GWAS studies, for which ethic approvement has been obtained. Consequently, no further ethical approval was necessary.

949 senescence-related genes were extracted from the CellAge (https://genomics.senescence.info/cells/) database (Build 3) using the keyword “cell senescence”. QTLs can uncover the relationships between SNPs and variations in DNA methylation, gene expression, and protein abundance. Blood eQTL summary statistics were obtained from eQTLGen, encompassing genetic data of blood gene expression in 31,684 individuals from 37 datasets.¹⁸ Blood mQTL summary data were generated from a meta-analysis of two European cohorts: the Brisbane Systems Genetics Study (n = 614) and the Lothian Birth Cohorts (n = 1366).¹⁵ Data on genetic associations with circulating protein levels were sourced from a protein quantitative trait loci (pQTL) investigation involving 54219 individuals.¹⁷

Summary-Data-Based MR Analysis

SMR was employed to assess the association of senescence-related genes methylation, expression, and protein abundance with the risk of NAFLD. Leveraging top associated cis-QTLs, SMR achieved enhanced statistical power compared to conventional MR analysis, particularly in scenarios with large sample sizes and independent datasets for exposure and outcome. Cis-QTLs were selected based on a ±1000 kb window around the gene of interest and a significance threshold of 5.0×10⁻⁸.¹⁹ SNPs with allele frequency differences exceeding 0.2 between datasets were excluded. Thresholds for pQTL, mQTL, and eQTL were set at 0.05. The original version of SMR only uses the lead cis-QTL variant as IV, and it has since been extended to SMR-multi to accommodate the potential presence of multiple cis-xQTL causal variants.¹⁵

In addition to exploring the causal associations between QTLs and NAFLD, the study further investigated the causal relationships between mQTL as the exposure and eQTL as the outcome. The key findings linking mQTL and eQTL with NAFLD are highlighted as signals of particular interest between mQTL and eQTL. Additionally, this study extends to the causal connections between eQTL and pQTL, with a focus on key genes from the mQTL-eQTL association and significant findings from NAFLD GWAS analysis associated with pQTL.

To differentiate between pleiotropy and linkage, we employed the HEIDI test, with P-HEIDI <0.05 indicating potential pleiotropy and leading to exclusion from the analysis. Associations meeting the criteria (p SMR < 0.05, multi-SNP-based P-value < 0.05 and P-HEIDI > 0.05) were considered for colocalization analysis in mQTL, eQTL and pQTL datasets.

Colocalization Analysis

We conducted colocalization analyses using the R package “coloc” to identify shared causal variants between NAFLD and the mQTLs, eQTLs, or pQTLs of senescence-related genes. In these analyses, five different posterior probabilities are reported, corresponding to the following hypotheses: H0 (no causal variants for either trait), H1 (a causal variant for gene expression only), H2 (a causal variant for disease risk only), H3 (distinct causal variants for two traits), and H4 (the same shared causal variant for both traits).²⁰ When GWAS signals and QTLs are found to colocalize, it suggests that the GWAS locus may influence the complex trait or disease phenotype by modulating gene expression or splicing.^21,22 For colocalization analysis, all SNPs within 1000 kb upstream and downstream of each top cis-QTL were retrieved to determine the posterior probability of H4 (PPH4). A PPH4 > 0.5 was used as the cut-off, indicating strong evidence of colocalization between GWAS and QTL associations.²³

Cell Culture and Treatments

The human liver-7702 (HL-7702) cell line was obtained from the Cell Bank of the Type Culture Collection of the Chinese Academy of Sciences (Shanghai, China). The complete culture medium for the HL-7702 cells consisted of DMEM/F-12 (1:1) (Gibco, 11330–032) with 89 mL ITS liquid medium (Sigma, I3146), 1 mL dexamethasone (Sigma, D4902-100mg), and 10 mL FBS (Gibco). The cells were cultured at 37°C in a 5% CO2 incubator. Once the cells reached 60–70% confluence, they were divided into two groups (n=3): (1) Control group (treated with normal saline for 24 hours) and (2) NAFLD group (treated with 1 mM oleic acid (OA; Sigma, USA) for 24 hours). Cell conditions were assessed using Oil Red O staining.

Creation of NAFLD Mouse Model and Histological Process

Six 8-week-old male, C57BL/6 WT mice, were utilized in this experiment. In the experimental group, male C57BL/6 mice were given a diet high in fat, sugar, and cholesterol, along with a high-sugar solution (23.1g/Ld fructose and 18.9g/Ld glucose) and a weekly low dose (0.2 ul /g) of carbon tetrachloride (dissolved in olive oil) administered intraperitoneally. After 16 weeks, NAFLD/NASH mouse models were established. In the control group, male C57 BL/6 mice were given a standard maintenance diet and a weekly intraperitoneal injection of the same dose of olive oil as the experimental group.

After 16 weeks, all mice were euthanized, and blood was drawn from the inferior vena cava using a 1 mL syringe and centrifuged at 3000 rpm for 15 minutes. The supernatant was collected to obtain mouse plasma. Serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels were measured using an automatic biochemical analyzer (ANTECH Diagnostics, Los Angeles, CA, USA). Liver tissue samples were also collected from the mice. A portion of each liver sample was immediately frozen in liquid nitrogen in an EP tube. The remaining tissue was fixed in formalin, embedded in paraffin, and stained with hematoxylin and eosin (HE). A section of the freshly frozen liver tissue, 8 μm thick, was stained with Masson. All animal experiments received approval from the Institutional Animal Care and Use Committee of Guilin Medical University (GLMC-IACUC-20241090). All animal experiments strictly adhered to the National Standards for Laboratory Animal Welfare issued by the Chinese government (GB/T 35892–2018) and the Guide for the Care and Use of Laboratory Animals (National Research Council, 8th Edition, 2011).

Quantitative Reverse Transcription-Polymerase Chain Reaction (qRT-PCR)

The frozen liver tissue was weighed, lysed, homogenized, and mixed with anhydrous ethanol. RNA was extracted from both mouse liver tissues and the HL-7702 cell line using TRIzol reagent (VAZYME, China). After extraction and elution through an RNA binding column, purified total RNA samples were obtained. cDNA was synthesized using the first strand cDNA synthesis kit. SYBR Green qRT-PCR premix was used for quantitative PCR, with gene expression levels normalized to GAPDH. RNA reverse transcription was performed with the PrimeScript™ RT Reagent Kit (VAZYME, China), and qRT-PCR was conducted using an FX Connect system (VAZYME, China) and SYBR^® Green Supermix (VAZYME, China). qRT-PCR was performed in triplicate, with primer details provided in Supplementary Table 2.

Statistical Analysis

All statistical analyses were performed using R (v4.3.0). The R package “ggplot2” and “ggrepel” was used for Manhattan plot generation, and “forestplot” for forest plot generation. The code for SMRLocusPlot and SMREffectPlot was sourced from Zhu et al.¹⁴

Results

Senescence-Related Genes Methylation and NAFLD

Results for causal effects of senescence-related genes methylation on NAFLD were visualized in Figure 2A (See full results in Supplementary Table 3). A total of 143 methylation loci (58 genes) passed the screening criteria (P-SMR < 0.05, multi-SNP-based P-value < 0.05 and P-HEIDI > 0.05). Of the identified signals, 40 near 13 unique genes were found to have strong colocalization evidence support (PPH4 >0.5) including ENDOG (cg13630871), S100A6 (cg24155129, cg01910639) and TP5313 (cg14273083). Specifically, ENDOG methylation at cg13630871 (OR = 1.02, 95% CI = 1–1.04) was linked to an increased risk of NAFLD. Conversely, certain methylation loci exhibited divergent association with NAFLD, such as S100A6, with cg24155129 (OR = 0.94, 95% CI = 0.9–0.98) linked to a decreased incidence of NAFLD and cg01910639 demonstrating the opposite (OR = 1.03, 95% CI = 1.01–1.06). The colocalization for representative methylation loci and NAFLD was visualized in Figure 2B. Among these identified CpG sites, the association for CD34 (cg15031826), PPARG (cg04632671), FOXP1 (cg06175008), TACC3 (cg10756475), FGFR3 (cg07041428, cg25342568, cg01464969, cg14661159, cg14101193, cg07458712) were replicated in the NAFLD replication cohort (FinnGen). The detailed associations in the NAFLD, NASH and liver cirrhosis replication cohorts were provided in Supplementary Tables 4–6.

Figure 2 SMR analyses of the causal effects of senescence-related genes mQTL on NAFLD. (A). Forest plot depicting the association between representative gene methylation and NAFLD. *Indicated causal associations supported by colocalization evidence. (B) Locus comparison plots between a representative gene (TP53I3) methylation loci and NAFLD. The scatter plot compares -log10(p) values from GWAS (x-axis) and mQTL (y-axis) analyses. Each point represents a SNP, with color indicating linkage disequilibrium with the lead SNP (highlighted in purple).

Senescence-Related Genes Expression and NAFLD

Causal effects of senescence-related genes expression on NAFLD were presented in Figure 3A (See full results in Supplementary Table 7). A total of 16 genes were found to be associated with NAFLD (P-SMR < 0.05, multi-SNP-based P-value < 0.05 and P-HEIDI > 0.05), in which S100A6, DTL, DNMT3A, ATG7, THRB, EGR2, FOXO1 and CHEK2 were positively associated with NAFLD incidence. Specifically, S100A6 (OR = 1.11, 95% CI = 1.04–1.19) was a potential risk factor for NAFLD and ENDOG (OR = 0.99, 95% CI = 0.97–1) exhibited the opposite. Among the loci corresponding to these genes, colocalization between representative genes and NAFLD was visualized (PPH4 > 0.5) (Figure 3B and C). Among the identified genes, none of them were replicated in the NAFLD cohort, NASH cohort and liver cirrhosis cohort (Supplementary Tables 8–10).

Figure 3 SMR analyses of the causal effects of senescence-related genes eQTL on NAFLD. (A) Forest plot depicting the association between representative gene expressions and NAFLD. *Indicated causal associations supported by colocalization evidence. Locus comparison plots between (B) ENDOG and (C) TP53I3 expression and NAFLD. The scatter plot compares -log10(p) values from GWAS (x-axis) and eQTL (y-axis) analyses. Each point represents a SNP, with color indicating linkage disequilibrium with the lead SNP (highlighted in purple).

Senescence-Related Protein Abundance and NAFLD

Causal effects of senescence-related protein abundance on NAFLD were presented in Figure 4A (See full results in Supplementary Table 11). In total, 6 proteins were found to be associated with NAFLD at the criteria (P-SMR < 0.05, multi-SNP-based P-value < 0.05 and P-HEIDI > 0.01), in which EIF2AK3, TIGAR and ING1 were positively associated with NAFLD incidence. Specifically, ING1 (OR = 1.16, 95% CI = 1.02–1.31) was a potential risk factor for NAFLD. Colocalization analysis between representative proteins and NAFLD were visualized (PPH4 > 0.5) Figure 4B and C. Among the identified proteins, only TIGAR was associated with NAFLD in the replication cohort (FinnGen) (Supplementary Tables 12–14).

Figure 4 SMR analyses of the causal effects of senescence-related protein abundance on NAFLD. (A) Forest plot depicting the association between representative protein abundance and NAFLD. *Indicated causal associations supported by colocalization evidence. Locus comparison plots between the level of (B) ING1 and (C) TIGAR and NAFLD. The scatter plot compares -log10(p) values from GWAS (x-axis) and pQTL (y-axis) analyses. Each point represents a SNP, with color indicating linkage disequilibrium with the lead SNP (highlighted in purple).

Tissue-Specific Validation

We further explored the causal associations between gene expression and NAFLD in the liver tissues. The expression of ENDOG in the liver tissues was negatively associated with NAFLD (OR = 0.98, 95% CI = 0.97–1), which was consistent with the protective role suggested in the SMR analysis. The detailed information regarding the association between identified genes with NAFLD in the liver tissues was provided in Supplementary Table 15.

Multi-Omics Data Integration

By integrating blood mQTL and eQTL data, we performed SMR with the methylation loci of the common genes in mQTL-GWAS and eQTL-GWAS results as the exposure and the expressions of these genes as the outcome. At a stringent criteria (P-SMR < 0.05, multi-SNP-based P-value < 0.05 and P-HEIDI > 0.05), S100A6 methylation at cg24155129 (OR = 0.6, 95% CI = 0.49–0.73) and cg01910639 (OR = 1.35, 95% CI = 1.24–1.47) were associated with a decreased and increased expression of S100A6 respectively (Table 1). The detailed integrated associations were provided in Supplementary Table 16.

Table 1 Causal Effects of the Senescence-Related Gene Methylation on Gene Expression

We did not identify common proteins between intersecting genes between mQTL and eQTL, and pQTL-GWAS results. Therefore, no SMR analysis was performed with the eQTL as the exposure and the pQTL as the outcome.

Integrating the multi-omics level evidence, we found that S100A6 may be causally associated with NAFLD. In particular, the methylation site cg01910639 showed a positive correlation with NAFLD risk and positively regulated S100A6 gene expression, which was positively associated with NAFLD risk. Additionally, cg24155129, which was also negatively correlated with NAFLD risk, negatively regulated S100A6 expression. Therefore, we propose that the higher methylation levels at cg20552903 and lower methylation levels at cg24155129 upregulates S100A6 gene expression, leading to an increased risk of NAFLD.

To visualize the results of our SMR analysis, we created locus plots for S100A6 methylation, expression and NAFLD (Figure 5A and B). Furthermore, we also provided the effect plots confirming the effects between S100A6 methylation and expression and NAFLD (Figure 6).

Figure 5 Locus plots showing (A) S100A6 methylation and (B) S100A6, their locations within the chromosome (lower panel). The Y-axis indicated the negative log of the p-values instrumental in deeming this locus significant in the SMR analysis.

Figure 6 SMR effect plots for (A) S100A6, (B) methylation site cg01910639 and cg24155129, and their associations with NAFLD. cis-QTLs were marked by blue dots, while top cis-QTLs were highlighted in red triangles.

Validation of Candidate Genes in Mouse and Cell Models of NAFLD

To validate the findings from the analysis above, we conducted experiments using both mouse and cell models of NAFLD. We assessed the expression levels of S100A6, ENDOG and TP53I3 in cell cultures (normal and steatotic). Oil Red O staining revealed substantial lipid accumulation in the NAFLD group cells, marked by an increased number of fat droplets (Figure 7A). qRT-PCR analysis of mRNA levels showed a significant rise in the expression of S100A6 and TP53I3, and lower expression of ENDOG in the NAFLD group compared to the control group (Figures 7B).

Figure 7 Expression of the Key Genes in a Cell and Mouse NAFLD Model. The NAFLD mouse model was generated in C57BL/6J mice. Pair-fed mice were used as controls. Serum and liver tissues were collected on the 16 weeks for further analysis. (A) Oil Red O staining. (B) The relative mRNA expression of S100A6, ENDOG and TP53I3 in cell NAFLD model was verified by qRT‒PCR. (C) HE and Masson staining. (D) Serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels. (E) The relative mRNA expression of the S100A6 in mouse NAFLD model was verified by qRT‒PCR. N = 4 in mouse model and N = 3 in Cell model, *p < 0.05, **p < 0.01, ***p < 0.001.

In the animal model, we specifically focused on S100A6 due to multi-omics evidence suggesting that methylation at cg20552903 and/or cg24155129 might regulate its expression. H&E and Masson staining suggested hepatic steatosis in the NAFLD group (Figure 7C). AST and ALT levels were significantly higher in the NAFLD group than in the control group (Figures 7D), indicating successful establishment of the NAFLD model. qRT-PCR measurements revealed that the expression levels of S100A6 and ENDOG were significantly different in the NAFLD group compared to the control group (Figures 7E), suggesting their potential regulatory role in NAFLD development.

Discussion

In this study, we systematically investigated the causal relationships between the methylation, gene expression and protein abundance of senscence-related genes and NAFLD using a multi-omics approach and SMR analysis. We chose to use the term NAFLD, given the ongoing transition to MASLD terminology, to maintain consistency with historical datasets and clinical contexts. Integrated multi-omics evidence from blood mQTL and eQTL SMR analysis revealed 3 genes (S100A6, ENDOG and TP53I3) as potential causal genes associated with NAFLD. And we further confirmed these findings by validation in mouse and cell models of NAFLD.

At the mQTL and eQTL levels, S100A6 was found to be a potential risk factor for NAFLD. S100A6, also referred to as calcyclin, encodes a protein belonging to the S100 family and is integral to the regulation of cellular senescence. This gene has been shown to have an inhibitory effect on senescence-like changes in various cell types.²⁴ Its deficiency has been shown to induce morphological and biochemical features that are characteristic of cellular senescence.²⁵ Recently, there has been an ongoing research into the role of S100A6 in NAFLD. A recent study has identified a significant relationship between the liver-derived protein S100A6 and the progression of NAFLD.²⁶ Elevated serum levels of S100A6 were observed in both human patients with NAFLD and in a high-fat diet-induced mouse model, correlating negatively with β-cell insulin secretory capacity. Depletion of hepatic S100A6 in mice improved glycemia, suggesting a contributory role of S100A6 in the pathophysiology of diabetes associated with NAFLD. Additionally, a review by Delangre et al highlighted that the aberrant activity of S100 isoforms, including S100A6, contributes to the dysregulation of lipid metabolism leading to hepatic steatosis and insulin resistance (IR), which are hallmarks of NAFLD.²⁷ While the exact mechanisms are not fully elucidated, it was suggested that S100 proteins may influence cell proliferation, apoptosis, migration, and inflammation, which are all relevant to the pathophysiology of NAFLD. In our study, we discovered that higher levels of S100A6 might be associated with an increased risk of developing NAFLD, possibly by the dysregulation of lipid metabolism and promotion of hepatic steatosis. Furthermore, our findings propose a novel avenue for therapeutic intervention, where modulating S100A6 expression or its regulatory pathways could be explored as a strategy to slow or halt disease progression in NAFLD patients. Additional research is required to fully understand the complex role of S100A6 in hepatic health and disease, and to determine whether diminishing its effects could offer a viable treatment approach for those at risk of NAFLD.

In addition to S100A6, ENDOG was demonstrated to be a protective factor for NAFLD. ENDOG is a gene that encodes the mitochondrial protein Endonuclease G, a crucial enzyme involved in various cellular processes, particularly apoptosis and DNA metabolism. In the context of NAFLD, research has uncovered that ENDOG promotes NAFLD development via regulating the expression of lipid synthesis-associated genes like ACC1, ACC2, and FAS.²⁸ Loss of ENDOG was found to repress high-fat diet-induced liver lipid accumulation.²⁸ Therefore, targeting ENDOG could be a potential therapeutic approach for NAFLD. However, our study proposed the opposite, in which ENODG expression was negatively associated with NAFLD incidence. The controversy between ENDOG and NAFLD could be due to the multifactorial and dynamic nature of ENDOG in NAFLD pathogenesis. Additionally, the role of ENDOG might be context-dependent, with its expression and activity influenced by various environmental and genetic factors that could alter its function from protective to pathogenic, underscoring the complexity of its involvement in NAFLD.

TP53I3, also known as tumor protein p53 inducible protein 3, functions as a quinone oxidoreductase, which is involved in cellular redox reactions. Due to its role in apoptosis and stress responses, TP53I3 has been implicated in cancer research.²⁹ However, no direct evidence about TP53I3 in NAFLD has been presented. In this study, we demonstrated that TP53I3 expression was negatively associated with the incidence of NAFLD, suggesting it as a potential protective factor. We could postulate that TP53I3 is involved in the generation of ROS and participates in p53-mediated cell death pathways associated with NAFLD progression.

By integrating multi-omics analysis of mQTL and eQTL, we uncovered a potential regulatory axis in NAFLD pathogenesis: DNA methylation at specific loci suppresses S100A6 gene expression, reducing S100A6 protein levels and decreasing the susceptibility to NAFLD. This opens up new avenues for therapeutic intervention in NAFLD, such as targeting this regulatory axis to modulate gene expression. Potential interventions might include the use of methylating agents or therapies to reduce S100A6 expression. Additionally, the S100A6 methylation-S100A6 axis could serve as a biomarker for early detection, prognosis, and monitoring of therapeutic responses in NAFLD patients, thereby enhancing personalized clinical care.

This study represents the first evaluation of the associations between senescence-related genes and NAFLD using SMR and colocalization. The main strength of this study is its use of SMR, allowing simultaneous assessment of the associations between methylation, expression, and protein abundance of senescence-related genes and NAFLD in independent European populations. Additionally, colocalization approaches effectively eliminate potential bias caused by linkage disequilibrium. Additionally, GWAS datasets with large sample sizes increased the statistical power of our study. Nonetheless, some limitations have to be addressed. First, due to the limited number of senescence-related proteins in the pQTL dataset, the current study did not fully explore the causal relationship between senescence protein abundance and the risk of NAFLD. Second, the exclusive use of cis-QTLs in SMR analysis may limit the comprehensiveness of the identified genetic associations and overlook long-range regulatory effects relevant to NAFLD pathogenesis. Third, SMR also has limited ability to exclude horizontal pleiotropy, where a gene affects disease through pathways independent of expression. Fourth, the tissue-specific nature of eQTL/mQTL associations means that the relevance of the selected QTL tissues to the disease-affected tissues directly impacts the reliability of the findings. Fifth, conclusions should be treated with caution when extending to other populations, as this study was based solely on European ancestry. Lastly, the findings from SMR analysis, while valuable for identifying potential causal associations, may not fully reflect clinical observations. SMR relies on genetic data and statistical models, which may not capture the full complexity of biological pathways or the influence of environmental factors on NAFLD. Additionally, SMR reflects the lifelong exposure effects associated with genetic variants, which may differ from the short-term effects of interventions or environmental exposures. Therefore, the results need to be contextualized with observational or clinical studies to better understand their relevance and applicability in clinical settings.

Conclusions

Our findings suggest potential causal relationships between senescence-related gene methylation, expression, and protein abundance and NAFLD, with S100A6, ENDOG and TP53I3 emerging as notable candidates in NAFLD pathogenesis. These findings provide a foundation for future research endeavors and clinical applications, but further investigations are needed to confirm these associations and their therapeutic implications.

Abbreviations

GWAS, genome-wide association study; HEIDI, heterogeneity independent instruments; HEIDI, heterogeneity in the dependent instrument; HL-7702, Human Liver-7702; HE, hematoxylin and eosin; IVs, instrumental variables; MR, Mendelian randomization; NAFLD, Nonalcoholic fatty liver disease; NASH, nonalcoholic steatohepatitis; PPH4, posterior probability of H4; QTL, quantitative trait locus; qRT-PCR, Quantitative reverse transcription-polymerase chain reaction; SMR, summary-data Mendelian randomization; SASP, senescence-associated secretory phenotype.

Data Sharing Statement

The GWAS summary statistics for NAFLD can be accessed via the FinnGen and GWAS Catalog under the search term of GCST90275041 and GCST008469. The QTLs data for senescence-related genes can be obtained via CellAge.

Ethics Approval and Consent to Participate

According to Item 1 and 2 of Article 32 of “the Measures for Ethical Review of Life Science and Medical Research Involving Human Subjects”, this study is exempt from ethical review and approval, as it utilized summary statistics from public GWAS studies. All animal experiments received approval from the Institutional Animal Care and Use Committee of Guilin Medical University (GLMC-IACUC-20241090). All animal experiments strictly adhered to the National Standards for Laboratory Animal Welfare issued by the Chinese government (GB/T 35892-2018) and the Guide for the Care and Use of Laboratory Animals (National Research Council, 8th Edition, 2011).

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This study was funded by the First Affiliated Hospital of Guilin Medical University, PhD start-up fund, and The Project for Improving the Research Foundation Competence of Young and Middle-aged Teachers in Guangxi Universities (2025KY0526). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the paper.

Disclosure

The authors declare that they have no competing interests.

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Introduction

Acute Kidney Injury (AKI) is a rapidly progressive decline in glomerular filtration rate (GFR) indicated by a rise in serum creatinine (SCr) of 0.3mg/dl or more within 48 hours or to 1.5 times the baseline in 7 days and/or reduction in urine output (UOP) to less than 0.5 mL/kg/hour for at least 6 hours.¹ Globally, AKI affects approximately 13.3 million people, causing 1.7 million deaths each year.² AKI is not only a very common condition, but also a predictor of morbidity, and can cause chronic kidney disease (CKD) or progress to kidney failure, which further complicate patient management and worsen prognosis.³ However, AKI may be reversible if detected early enough.⁴ Critically ill patients are more likely to develop AKI. AKI in critically ill adults arises from hemodynamic, inflammatory, and nephrotoxic factors. Ischemic injury due to reduced renal perfusion is a common pathway, particularly in conditions like sepsis and shock. The resulting hypoxia and oxidative stress can cause direct tubular damage and apoptosis.⁵ Inflammatory cytokines and mediators released during systemic infections and critical illnesses contribute to endothelial dysfunction, increased vascular permeability, and further renal injury.³

Moreover, the administration of nephrotoxic agents, either as part of therapeutic regimens or inadvertently, exacerbates renal injury. Drugs such as aminoglycosides, contrast agents used in diagnostic imaging, and certain chemotherapeutic agents have well-documented nephrotoxic effects.⁶ The cumulative impact of these factors leads to the clinical manifestation of AKI.

Generally, AKI affects about 10–15% of in-hospital patients, and more than 60% of patients admitted in ICUs.^7–9 Severe AKI occurs in about 4–5% of critically ill patients^9–11 due to resistant volume overload, uncontrollable electrolyte disorders, uremic complications, and drug toxicity. The exact mechanism of how AKI influences the clinical outcomes in severely ill patients remains unclear. However, it’s thought that it induces multi-system inflammatory responses.¹² Variations in reported incidences across studies on AKI among critically ill patients are attributed to differences in the study population, geographical area of study, patient baseline characteristics, length of observation period, and the criteria used to determine AKI.^13–18 Patients who develop AKI have an 8.8 times higher risk of developing CKD, posing far greater long-term health and cost consequences.^7,19–21 Early recognition and management of AKI can prevent its major complications.^22,23 Because critically ill patients may develop multi-organ dysfunction, the development of AKI in this population may have an impact on the outcome. Despite the overwhelming morbidity caused by AKI in critically ill patients, data on the incidence and predictors of AKI in this patient population in Uganda is limited. Bagasha et al (2015) studied the prevalence of AKI among adult patients with sepsis on the medical ward of a national referral hospital in Uganda and found a prevalence of 16.3% and in-hospital mortality of 21%.²⁴ Similarly, Kimweri et al (2021) studied the incidence and risk predictors of AKI among HIV-positive patients with sepsis at Mbarara Regional Referral Hospital. In their study, the incidence of AKI in 48 hours was 19.2%.²⁵ However, the incidence and predictors of AKI among other critically ill patient populations in Uganda have not been studied. This study intended to assess the incidence and independent predictors of AKI among critically ill patients at Mbarara Regional Referral Hospital (MRRH) in southwestern Uganda. The study also aimed to describe the management of AKI and evaluate the treatment outcomes among patients with incident AKI. The study, ultimately, aimed to guide health workers and policy makers to innovate and implement strategies towards mitigating the burden of AKI and reducing the associated complications in critically ill patients.

Methods

Study Setting

The site of the study was Mbarara Regional Referral Hospital (MRRH) in Mbarara city of southwestern Uganda. This is a tertiary 600-bed health facility serving a population of at least 4 million people from Mbarara and neighboring districts, including those from the Masaka Health Region and neighboring countries in the south western area. It is also the teaching hospital for Mbarara University of Science and Technology, Mayanja Memorial Training Institute and Bishop Stuart University.

The major specialized services provided are Emergency medicine (surgical and medical), Community Health, Internal Medicine, Obstetrics & Gynaecology, Paediatrics, and Surgery. The hospital’s emergency ward accommodates both medical and surgical patients. Patients will typically be admitted to the emergency ward for stabilization before they are transferred to the general medical and surgical wards for continued care. The hospital also has an 8-bed capacity ICU which serves critically ill patients. It also has 2 nephrologists and can provide hemodialysis to patients who require the intervention.

Study Design

This was a prospective cohort study among critically ill in-patients between 1^st February and 30^th May 2024.

Study Population

All critically ill adult patients who provided informed consent by themselves or through their next of kin were recruited into the study. The critically ill patients who were diagnosed with AKI at admission, or were dialysis-dependent, got discharged, or died within the first 48 hours of admission were excluded. Eligible participants were recruited consecutively over the study period without predefined sample size calculation, as recruitment was based on availability of critically ill patients within the study timeframe. Post-hoc power analysis was done to assess the sufficiency of the sample size to answer the study objectives.

Data Collection and Procedure

The team of research assistants who collected data were taken through a one-week’s training on the study protocol and tools such as the National Early Warning Score 2 (NEWS-2). They also completed the Responsible Conduct of Research (RCR) and Ethics in Research training, to enhance safety of the participants and integrity of data.

We used the “National Early Warning Score 2 (NEWS-2)” to assess and determine the degree of illness of a patient thus prompting critical care intervention.²⁶ A patient with a NEWS score of 5 or more was considered to be critically ill and requiring prompt emergency assessment. Critical illness in this study was defined as a potentially reversible life-threatening condition in which there is a decline in the function of vital organs, and death is imminent in case of absence of appropriate care.²⁷ Accordingly, critically ill patients were screened and consecutively enrolled from the intensive care unit (ICU), resuscitation bays at the medical emergency and surgical emergency wards as well as the adult general medical and surgical wards. Upon admission, patients were assessed using the “National Early Warning Score 2 (NEWS-2)”.

After obtaining informed consent, the relevant data including socio-demographic characteristics, medical history, admission vitals, NEW 2 score, baseline serum creatinine (SCr), blood urea nitrogen and serum electrolytes, complete blood count, random blood sugar, AKI management, exposure to nephrotoxic drugs, current drug and alcohol use were collected using a structured data abstraction form. Follow-up serum creatinine measurements were done after 48 hours to assess for incident AKI. Blood samples were tested at the Mbarara Regional Referral Hospital laboratory which does routine external quality assurance.

The “Kidney Disease Improving Global Outcomes (KDIGO)” AKI definition as “an increase in serum creatinine by greater or equal to 0.3mg/dl within 48 hours”²² was used for this study. Whenever CKD was suspected, the diagnosis was made using evidence from past records when available, history including the duration of symptoms, urinalysis and hematological indices, and the use of kidney ultrasound to determine the kidney sizes. The ultimate decision was made in consultation with the nephrologist.

Participants were followed until discharge, death or day 7 from enrolment, whichever would come first.

Data Statistical Analysis

Data cleaning was done using Epi-Info, after which data was imported into STATA version 13 for analysis. Numeric variables were summarized using means or medians with their respective measures of dispersion according to their distribution. Categorical variables were summarized using frequencies and percentages. The Fisher exact test or χ2 test was used to compare the participants baseline characteristics. The incidence of AKI was computed by dividing the number of participants who incurred AKI by the total number of study participants. The total person-days was computed as a sum of the days each of the participants stayed in the hospital. We computed the incidence rate by dividing the total number of participants who developed AKI by the total person-days, then multiplied the quotient by 1000. Univariate and multivariate logistic regression analysis was used to identify the independent predictors of AKI. A p-value ≤0.05 was considered statistically significant.

Ethical Considerations

This study was carried out in compliance with the Declaration of Helsinki. The MUST Research and Ethics Committee approved the study [Reference No: MUST-2023-1235] and site clearance was given by the hospital administration. The patient, or caretaker for those who were too sick, gave written informed consent before enrollment of any participant.

Results

Over 4 months, 420 patients were screened. Out of these, 220 were excluded because they were not critically ill. Out of 200 eligible patients, 28 were admitted to the ICU while 172 were admitted to both the surgical and medical/emergency wards. Overall, 161 participants who satisfied the eligibility criteria were involved in the final analysis. This data is shown in the study flow diagram in Figure 1.

Figure 1 Study flow diagram showing the enrolment of patients.

Baseline Characteristics of the Study Participants

The study sample comprised 161 participants, of whom the majority were male (59.6%), without a statistically significant difference in gender distribution between those who developed AKI and those who did not (64.6% vs 35.4%, P=0.432). The overall median age was 48 years (IQR: 31–65). The highest number of participants (52.8%) were admitted in the general medical or medical emergency wards, and only 11.2% of participants were admitted in the ICU.

There was a statistically significant increase in AKI incidence among participants who had been recently hospitalized within the past 3 months. About, 76.1% of the participants who developed AKI had reported a history of previous hospitalization compared to 23.9% of those without AKI (P=0.021). This data is elaborated in Table 1.

Table 1 Baseline Characteristics of Study Participants

Clinical Characteristics of Study Participants

Among the notable co-morbidities, hypertension was the most prevalent, affecting 26.1% of participants overall. However, the prevalence of comorbidities between the participants who incurred AKI and those who did not was comparable. The overall mean NEWS-2 (National Early Warning Score 2) was 11.9 (SD: 2.4), suggesting a high risk of clinical deterioration among participants. However, the comparative analysis did not show a statistically significant difference in the NEWS 2 scores of the “AKI” and “No AKI” groups (P = 0.102).

Among the laboratory parameters, the median WBC count was significantly higher in AKI patients (12.7 × 10⁹/L) compared to non-AKI patients (10.1 × 10⁹/L) (P=0.006). The median creatinine level at 0 hours was also significantly higher in AKI patients (1.0 mg/dl) compared to non-AKI patients (0.9 mg/dl) (P=0.028). This data is summarized in Table 2.

Table 2 Clinical Characteristics of the Study Participants (N=161)

Incidence of Acute Kidney Injury Among Critically Ill Patients

Among all the 161 participants who were followed up for a median duration of 6 days (IQR: 4–10), the incidence rate of AKI was 70 (95% CI 55–90) per 1000 person days of observation (Table 3). Out of the 100 participants who developed AKI, 84% (84/100) had stage 1 AKI, while those with stage 2 were 8/100 (8%) and stage 3 were 8/100 (8%).

Table 3 Incidence of Acute Kidney Injury Among Critically Ill Adults

Medications Used Among Critically Ill Patients Admitted at Mbarara Regional Referral Hospital

Out of 161 study participants, about 96 (60%) were exposed to at least one (mean, 0.8) potentially nephrotoxic drug during hospitalization (Figure 2). A total of 127 (39.1%) drugs used by the study participants during hospitalization were deemed potentially nephrotoxic. Penicillins, Angiotensin receptor blockers (ARBs), loop diuretics, proton pump inhibitors, Angiotensin converting enzyme inhibitors (ACEIs), and Tenofovir Disoproxil Fumarate, and Phenytoin being the most commonly used. See Figures 3 and Box 1.

Box 1 Other Medications Used by Critically Ill Patients During Hospitalization

Figure 2 Prevalence of potentially nephrotoxic drug exposure among study participants.

Figure 3 Commonest drugs used by study participants.

Predictors of AKI Among Critically Ill Patients

Patients who developed AKI had higher odds of having been previously hospitalized within the last 3 months, with a significant association observed in both univariable (cOR 2.44, 95% CI: 1.13–5.29, P=0.023) and multivariable analyses (aOR 2.56, 95% CI: 1.08–6.06, P=0.032). Admission to the surgical ward was another significant predictor of AKI (aOR 4.32, 95% CI: 1.22–15.24, P=0.023). Further, elevated serum creatinine at 0 hours (≥1.2 mg/dl) significantly increased the odds of developing AKI (aOR 2.44, 95% CI: 1.13–5.27, P=0.023). Similarly, a baseline WBC count ≥12 x10⁹/L was a significant independent predictor of AKI (aOR 2.57, 95% CI: 1.21–5.46, P=0.014) (Table 4).

Table 4 Predictors of Incident AKI Among Critically Ill Adults

Management and Outcomes of Incident AKI Among Critically Ill Adult Patients

Out of 100 participants who developed AKI, only 2% (n=2) underwent hemodialysis. The rest of the patients were managed conservatively. The mortality rate among the patients who developed AKI was 25% (25/100) compared to 13.1% (8/61) among the non-AKI group. Of the patients who survived, 19% (n=14) had complications secondary to the underlying illnesses. The commonest complication was hemiplegia (n=12). Long-term effects of AKI could not be assessed during the follow-up period of 7 days. Despite not having shown statistical significance in multivariable analysis, the patients who developed AKI had up to 2.2 more odds of dying than the ones who did not (OR 2.20, 95% CI: 0.92–5.27, P=0.074). The median overall time of hospitalization was 6 days (IQR: 4–10) with no significant difference between those who had AKI (6 days, IQR: 4–10) and those without AKI (7 days, IQR: 5–9.5) (P=0.307).

Discussion

Incidence of Acute Kidney Injury

We found an overall incidence rate of AKI of 70 (95% CI 55–90) per 1000 person days of observation among critically ill adult patients. This high incidence indicates the susceptibility of critically ill patients to AKI. Patients in low- and middle-income countries have a higher incidence of AKI than those in high-income countries. For example, Ashine et al conducted a retrospective follow-up study in Central Ethiopia, which revealed an incidence rate of AKI of 30.1 per 1000 person-days of observation.²⁸ We found the incidence rate of AKI in our population to be twice as high as that reported by that study. Conversely, a study by Susantitaphong et al (2013) in the United States reported incidence rates of AKI between 5% and 7% in critically ill patients. This is due to differences in patient demographics, healthcare practices, and underlying comorbidities.²⁹ Furthermore, majority of the patients had stage 1 AKI. Therefore, optimum patient care may be achieved by early recognition of AKI and initiating timely interventions which may prevent further kidney damage.³⁰

Predictors of Incident AKI Among Critically Ill Adult Patients

Previous hospitalization in the last 3 months was a strong predictor of AKI. This finding aligns with previous studies that showed a history of hospitalization to increase the risk of AKI, likely due to pre-existing comorbidities and exposure to nephrotoxic agents during previous hospital stays.³¹

The odds of developing AKI also significantly increased among patients admitted to the surgical ward. This could be resulting from postoperative complications and exposure to nephrotoxic medications among these patients. Hobson et al (2015) showed that patients with surgical conditions are at a risk for AKI due to fluid shifts and blood loss.³² This highlights the importance of monitoring and correcting fluid imbalances among surgical patients.

Patients with higher creatinine levels at admission also had higher odds of developing AKI. Coca et al (2009) had similar findings in their research which established baseline renal function as a critical determinant of AKI.³³ Patients with underlying renal impairment are likely to be more vulnerable to further insults during critical illness.

Having higher baseline white blood cell (WBC) count also significantly increased the odds of developing AKI. A raised WBC may reflect an underlying inflammatory state or infection contributing to AKI. Inflammatory processes contribute to the pathogenesis of AKI, and elevated WBC counts have been revealed to be a risk factor in previous studies as well.³⁴

Potentially Nephrotoxic Medications Use

In our study, more than half (60%) of study participants were exposed to potentially nephrotoxic medications during hospitalization. Avoidance or dose-adjustment of potentially nephrotoxic drugs is recommended in the acute care settings. However, lack of clear evidence for choice of equally effective but less nephrotoxic alternative drugs leads to inevitable use of nephrotoxic drugs like proton pump inhibitors, loop diuretics, NSAIDs, and some beta-lactam antibiotics like piperacillin/tazobactam. The high prevalence of nephrotoxic drug use and the diverse mechanisms of renal injury caused by these drugs might explain the high incidence of AKI in critically ill patients in resource-limited settings.

Management and Outcomes of Patients with Incident AKI

Only 2% of the patients who developed incident AKI were managed with hemodialysis. This is because hemodialysis bills at our hospital are met by the patients and their families, most of whom have no health insurance and therefore, cannot afford the service. On the contrary, higher rates of specialized treatments like hemodialysis are seen in high-income countries. This difference can be explained by the readily available resources and the different thresholds for initiating dialysis.²

Death occurred in 25% (25/100) of those who developed AKI compared to 13.1% (8/61) in the No-AKI group. Similarly, studies by Bellomo et al (2017) and Coca et al (2015) have shown high mortality rates among patients with AKI. The pooled mortality rate due to AKI in a meta-analysis by Susantitaphong, P.et al (2013) was 23.9%.^29,35,36

We found no difference in length of hospital stay between the AKI and No-AKI groups. However, other studies have demonstrated longer durations of hospital stay among patients with AKI. This has been attributed to the development of complications and the need for closer monitoring and management among these patients.^29,37

Limitations

Only one study site was used for this study, limiting the generalizability of the findings. We only measured serum creatinine at 2 time points; at admission and 48 hours later. This limited our follow-up of patients for the onset of AKI beyond 48 hours of admission. Serum creatinine takes time to increase, therefore, using its levels to detect AKI might lead to missing the detection in some cases. The short duration of follow-up for a maximum of 7 days did not allow for assessment of reduction in urine output during the study period and the long-term effects of AKI. Additionally, we did not assess the dosage and duration of exposure to individual nephrotoxic drugs. This limited our assessment of the causative relationship between nephrotoxic drug exposure and AKI incidence. Subsequent studies should focus on addressing these limitations to provide more insight into this topic.

Conclusions

There is a high incidence of AKI among critically ill patients. We found an incidence rate of 70 (95% CI 55–90) per 1000 person days of observation in this study.

A history of previous hospitalization within the past 3 months, having a baseline serum creatinine above 1.2 mg/dl, a white blood cell counts above 12 × 10⁹/L, and being admitted to the surgical ward were independently associated with incident AKI.

The majority of the patients with incident AKI received conservative management while only 2% underwent hemodialysis. A quarter of the participants with incident AKI died in hospital.

Recommendations

These findings highlight the importance of considering previous hospitalization, surgical admission, elevated baseline creatinine, and WBC counts as key predictors of AKI in critically ill patients. Prioritization of critically ill patients according to the number of these risk factors and subsequent closer monitoring might serve in the prevention, early diagnosis, and timely management of AKI, thus mitigating the burden of AKI in this vulnerable population.

We also recommend interventions at all levels to make RRT, particularly hemodialysis, more accessible and affordable in centers that manage critically ill patients to provide higher standard care and improve patient outcomes, especially among those with stage 3 AKI. For instance, one priority should be setting up a dialysis center at every regional referral hospital, training and employing dialysis nurses and equipping these centers with reagents and maintenance to minimize out-of-pocket expenditure for RRT services.

Furthermore, we recommend the implementation of standardized protocols for the management of AKI, and multidisciplinary care to optimize clinical outcomes and reduce mortality rates.

Further research on this topic should focus on studying the long-term effects of AKI in critically ill patients both during and after admission.

Abbreviations

AKI, Acute Kidney Injury; BMI, Body Mass Index; BP, Blood Pressure; CBC, Complete Blood Count; CKD, Chronic Kidney Disease; DM, Diabetes Mellitus; eGFR, Estimated Glomerular Filtration Rate; GCS, Glasgow Coma Score; GFR, Glomerular Filtration Rate; HIV, Human Immunodeficiency Virus; HT, Hypertension; ICU, Intensive Care Unit; KDIGO, Kidney Disease Improving Global Outcome; MAP, Mean Arterial Pressure; MRRH, Mbarara Regional Referral Hospital; MUST, Mbarara University of Science and Technology; RLS, Resource Limited Setting; RR, Respiratory Rate; RRT, Renal Replacement Therapy; SCr, Serum Creatinine; SSA, Sub-Saharan Africa; TDF, Tenofovir Disoproxil Fumarate; UOP, Urine Output; WHO, World Health Organization.

Data Sharing Statement

The complete datasets for this study will be availed by the corresponding author on request.

Acknowledgments

We acknowledge and appreciate all the study team members, hospital administration, and the participants for their consent to participate in the study.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This study was funded by the Internal Research Funds from the Mbarara University of Science and Technology Directorate of Research and Graduate Training (Grant number DRGT/SG/FY23-24/R4/T2P15).

Disclosure

The authors have no competing interests for this work.

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Asrar A. Alahmadi, MBBS, is heading a first-in-human, open-label, phase 1 clinical trial (NCT06801002) investigating JBZ-001, an orally bioavailable small-molecule inhibitor of dihydroorotate dehydrogenase (DHODH) for the treatment of patients with advanced solid tumors and non-Hodgkin lymphoma.

Developed by Jabez Biosciences, JBZ-001 targets the de novo pyrimidine biosynthesis pathway by inhibiting DHODH, the rate-limiting enzyme in this pathway. Preclinical studies demonstrated JBZ-001’s broad antitumor activity across a range of cell lines and patient-derived xenograft models, particularly in aggressive cancers such as lymphoma, leukemia, and small cell lung cancer. The drug showed higher potency and lower toxicity compared with other agents in its class, suggesting its potential as both monotherapy and in combination with chemotherapy.

The phase 1 study now follows a standard 3+3 dose escalation design to determine the optimal biological dose (OBD), rather than the maximum tolerated dose. Pharmacokinetic and pharmacodynamic assessments include a novel blood-based biomarker to monitor DHODH activity in real time. Once OBD is reached, expansion cohorts will explore efficacy signals in specific tumor types.

The trial is currently enrolling patients, with future plans for combination studies and phase 2 trials in promising indications such as small cell lung cancer and hematologic malignancies, according to Alahmadi, assistant professor at The Ohio State University and lead principal investigator at The Ohio State University Comprehensive Cancer Center-James Cancer Hospital & Solove Research Institute.

In an interview with Targeted Oncology^TM, Alahmadi discussed the trial in progress and key goals of the study.

Targeted Oncology^TM: Can you discuss the mechanism of action of JBZ-001?

Alahmadi: The drug is a small molecule that inhibits a very critical enzyme involved in DNA and RNA synthesis, which is essential in rapidly dividing cells. That’s essentially what cancer is—unchecked, rapid cell proliferation. The molecule blocks DHODH, which is part of the pyrimidine biosynthesis pathway. This pathway is vital for cell survival during rapid division. The inhibition shows promising results against aggressive cancers like lymphoma and leukemia, and we’ve also seen potential in some relapsed solid malignancies like small cell lung cancer.

What preclinical data supported the initiation of this phase 1 study?

We observed efficacy in cell lines, patient-derived xenograft models, and through large-scale screening across multiple malignancies. We demonstrated broad activity, which we believe is due to the drug’s mechanism. It targets cancer cell metabolism, giving it a wide scope of action.

What is the study design, including the planned dose-escalation scheme and patient eligibility?

This is a first-in-human study. We’re testing the drug as a monotherapy using a standard 3+3 dose-escalation design. We’re collecting pharmacokinetic and pharmacodynamic data, including a blood-based marker—DHO/DHODH level—which correlates with enzyme activity. Our aim is to identify the optimal biological dose, rather than just the maximum tolerated dose. Once we reach OBD, we will proceed with expansion cohorts, especially in areas where we observe potential efficacy.

What are the key goals of the study? What are you hoping to learn about the tolerability of the drug?

We want to assess safety and tolerability, of course. Preclinical data suggest this agent may be more potent and less toxic than other DHODH inhibitors. We’re hoping this translates into the clinic—more efficacy with lower toxicity. Looking ahead, we aim to test it in combination with chemotherapy to enhance treatment outcomes in solid tumors. We also want to determine the optimal biological dose that balances efficacy and safety, which aligns with the FDA’s Project Optimus initiative.

Assuming the safety and preliminary efficacy are favorable, what are the potential next steps in the clinical development of this agent?

The next step would be testing in combination with chemotherapy. Also, if we observe efficacy signals in specific cancer types, we’ll open expansion cohorts and move into phase 2 trials. For example, we’re already seeing encouraging signs in small cell lung cancer. There’s evidence from other groups, including large CRISPR-based screens, suggesting that small cell lung cancer heavily depends on the pyrimidine biosynthesis pathway—making this an attractive target.

What are the key takeaways about this study and what should oncologists know about recruitment or the drug itself?

So far, our preclinical data suggest this drug may be more potent than other DHODH inhibitors. We’re currently enrolling patients in the dose expansion phase. I think that’s a key point.

I think we can always do better in our fight against cancer. Over the years, clinical trials have brought promising new treatments to market—treatments that genuinely improve patients’ lives, like targeted therapies and immunotherapies. With this development, we hope not only to show this drug is effective, but also to identify biomarkers of response. That way, we can deliver the right drug to the right patient at the right time.

REFERENCE:

Alahmadi A, Bennett C, Biglione S, et al. Abstract CT199: An open-label phase 1 study to investigate JBZ001 in adults with advanced solid tumors and non-Hodjkin lymphoma (JBZ001, trial in progress). Cancer Res. 2025;85 (8_Supplement_2): CT199. doi:10.1158/1538-7445.AM2025-CT199

Introduction

Antimicrobial resistance has become a global concern, posing significant challenges in the treatment of infectious diseases.¹ Beta-lactam antibiotics, including penicillins, cephalosporins, carbapenems, and aztreonam, are commonly prescribed for bacterial infections.² However, the widespread use of third generation cephalosporins to combat Gram-negative bacterial infections has contributed to the development of resistance against beta-lactam antibiotics.³ Consequently, this has led to the emergence of organisms producing Extended-Spectrum Beta-Lactamases (ESBLs) and carbapenemase, further exacerbating the issue.⁴ In 2024, the World Health Organization (WHO) classified carbapenem-resistant Pseudomonas aeruginosa, carbapenem-resistant Acinetobacter baumannii, and Carbapenem-resistant Enterobacterales (CRE) as “critical” and “high” priority pathogens.⁵

The spread of antibiotic-resistant organisms in health care facilities and in the community is a worrying epidemiological problem that could be influenced by the rate of faecal carriage of antibiotic-resistant organisms.⁶ The Enterobacterales, regardless of whether they are community or hospital-acquired, primarily originate from the digestive tract.⁷ This area is a hub for the exchange of resistance genes and antibiotic treatments, which can lead to the overgrowth of resistant bacteria.⁸ When antibiotic resistant isolates colonize the intestinal compartment, the risk of infection due to these bacteria significantly increases.⁹ Additionally, the intestinal compartment may act as a source for ESBL- and carbapenemase-producing resistance genes, which can spread to other Enterobacterales through horizontal transmission.¹⁰ Hence, being aware of the frequency of patients carrying resistant bacteria in their digestive tracts can serve as a means of forecasting the extent to which these bacteria may cause infections and spread among people.^11–13In the past decade, there has been a global rise in reported cases of hospital-acquired infections caused by Gram-negative bacilli (GNB) that are resistant to carbapenem antibiotics.^14–17 Hospitalized patients face a double burden as they not only have to deal with their health problems but also with a severe infection of this scale. Hence, this critical situation is a matter of concern for all healthcare systems.^18,19

In Africa, ESBL-PE (ESBL Producing Enterobacterales) carriage in the gastrointestinal tract ranges from 10% to 50%, highlighting the severity of the situation.²⁰ In Benin, a West African country, government has formally validated its National Action Plan (NAP); however, there are ongoing efforts to implement these measures, and as of now, the NAP has not yet been included on the WHO NAP website.²¹ More resources are required to develop laboratory testing capacity for the accurate detection of antibiotic-resistant bacteria throughout the country.²² While several studies have reported systematic data about AMR prevalence in general or focused on specific sample types, there is a lack of data regarding fecal carriage of antibiotic-resistant bacteria.^23–28 Recent studies in Benin have reported high rates of multidrug-resistant pathogens and revealed the first detection of a plasmid-encoded New-Delhi metallo-beta-lactamase-1 (NDM-1) producing Acinetobacter baumannii from surgical site infections within hospitals.^29,30 Prior to this, a study reported the first occurrence of hospital-originated Pseudomonas aeruginosa producing carbapenemase VIM-2.³¹ Additionally, there is an increased risk of infection associated with care,^24,32 and wound contamination by hospital bacteria.^24,33 Therefore, this study aims to determine the one-day prevalence of faecal carriage of ESBL and carbapenemase producing Gram-negative bacilli, along with associated risk factors and to characterize the resistant isolates.

Methods

Study Design and Sampling

This cross-sectional study, conducted in December 2022, focused on fecal samples from post-surgery patients (accidents and injury-related cases) in tertiary hospitals in Benin. A consecutive sampling approach was employed, where stool/rectal samples were collected from every consenting patient who had been hospitalized for more than 48 hours, until the target sample size of 30 was reached in each hospital. This 48-hour cutoff was used to distinguish hospital-acquired from community-acquired colonization. Four hospitals (Central Hospital and University of Mother and Child Lagoon of Cotonou, Departmental Hospital Center of Zou and the Hills, Departmental Hospital Center of Borgou-Alibori, and Hospital Saint Jean de Dieu of Tanguiéta) participated in the study, with patients with digestive pathologies excluded. The sample size was determined based on the bed capacity of the surgery wards in each hospital, ranging from 39 to 47 beds. All samples were collected on the same day, following strict aseptic protocols. A questionnaire was administered to gather sociodemographic information, clinical data, and medical history. The study received prospective ethical approval from the Ethics and Research Committee of the Institute of Applied Biomedical Sciences (CER-ISBA) under approval number 154. Written informed consent was obtained from each participant or their parent/guardian before participation, with a clear explanation of the study’s objectives. All research activities were conducted in accordance with the Declaration of Helsinki.

Species Identification, Antibiotic Susceptibility Testing, ESBL and Carbapenemase Detection

Fecal samples were screened for betalactam and carbapenem resistance using CHROMID^® ESBL and CHROMID^® CARBA SMART agar media (Bio-Rad, USA). After 24 hours of incubation at 37°C, distinct colonies were selected based on their characteristic color on the selective media. Pure cultures were obtained using again CHROMID^® ESBL and CHROMID^® CARBA SMART agar. Gram-negative bacilli, excluding Stenotrophomonas maltophilia, were identified using Matrix-Assisted Laser Desorption Ionization–Time of Flight (MALDI-TOF) mass spectrometry (Bruker Daltonics, Bremen, Germany). Antibiotic susceptibility testing was performed with inoculum from a subculture on Mueller–Hinton agar using the disc diffusion method on Mueller–Hinton agar, following recommendations from the European Committee on Antimicrobial Susceptibility Testing.³⁴ Various antibiotic discs manufactured by Bio-Rad were used for the testing. ESBL production was confirmed using the double disk synergy technique. For Enterobacterales, discs (Bio-Rad, United States) containing cephalosporins including cefotaxime 30 µg, ceftazidime 30µg, cefepime 30µg were applied next to clavulanic acid disc following recommendations from EUCAST.³⁵ For Pseudomonas spp, imipenem double-disc synergy test (DDS-IPM) with discs containing ceftazidime 30µg, cefepime 30µg and a disc with imipenem 10µg as the ESBL inhibitor was used.³⁶ Acinetobacter spp were tested for ESBL production by placing discs containing piperacillin (30 μg) and a combination of piperacilline + tazobactam (30–6µg) on agar plates.³⁷ Resistance to one of carbapenems tested was used as an indicator for the continuation of tests for detecting carbapenemase production. On these strains, the carbapenemase production test was carried out using the rapid diagnostic kit NG-TEST CARBA 5 (Hardy Diagnostics, California, United States) capable of detecting KPC, OXA-48-like, VIM, IMP, and NDM carbapenemases. Reference strain of Escherichia coli ATCC 25922 was used as quality control for the antibiotic susceptibility test.

Detection of Antibiotic Resistance Genes

Bacterial DNA was extracted from strains isolated from ESBL agar using boiling method. Multiplex PCR was performed to identify isolates carrying betalactamase encoding genes,³⁸ carbapenemase encoding genes³⁹ and PMQR genes.⁴⁰ Each reaction was prepared in a total volume of 25 µL using OneTaq^® Quick-Load^® 2X Master Mix with Standard Buffer (Biolabs, South Africa) and following the manufacturer instructions. The DNA fragments underwent electrophoresis in a 2% agarose gel, and the results were interpreted by comparing the migration of the fragments to marker sizes. The reference strain Escherichia coli ATCC 25922 was used as a negative control, while clinical strains producing ESBL and carbapenemase served as positive controls for the PCR assays.

Statistical Analysis

Data entry was performed using Excel 2019, and statistical analysis was conducted using Stata SE 11 software. Univariate analysis was initially conducted, selecting variables with a p-value less than 0.05 as potentially associated with ESBL and carbapenemase producing bacteria carriage. Results were reported as odds ratio (OR) with corresponding 95% confidence intervals, using an interpretation threshold of α = 0.05. The chi-square test was also performed to determine the degree of association between factors and independent variables.

Results

Factors Associated with the Carriage of ESBL and Carbapenemase Producing Gram Negative Bacteria

Table 1 described the factors associated with the carriage of ESBL Producing Gram negative bacteria. Out of the 120 patients enrolled in the study, 46 were identified as carriers of ESBL-producing bacteria, resulting in a prevalence of ESBL carriage at 38.33%. The study of the association of variables shows a high degree of association (p < 0.05) with ESBL carriage for the factors: independent walking and use of a wheelchair (Table 1).

Table 1 Factors Associated with the Carriage of ESBL Producing Bacteria

Table 2 described the factors associated with the carriage of carbapenem-resistant bacteria. Out of the 120 patients enrolled in the study, 59 were identified as carriers of carbapenem-resistant bacteria, resulting in a prevalence of 49.16%. Analysis of the association between variables revealed no statistically significant associations (p > 0.05) among the factors studied (Table 2).

Table 2 Factors Associated with the Carriage of Carbapenem-Resistant Bacteria

Species Identification and Percentage of Antibiotic Resistance

Table 3 shows the results of the identification of the isolated bacterial species. A total of 92 strains were isolated using ESBL agar. Among the bacterial species isolated from ESBL agar, Escherichia coli (44.56%) was the most frequently isolated, followed by Pseudomonas aeruginosa (26.08%) and Acinetobacter baumannii (10.87%). Notably, all Enterobacter cloacae strains (100%), most Escherichia coli strains (82.93%), and Klebsiella pneumoniae strains (80%) were found to be ESBL producers. A total of 83 strains were isolated using carba agar. The identification of isolates from carba agar showed a predominance of the species Escherichia coli (44.45%) followed by the species Klebsiella pneumoniae (16.06%) and Acinetobacter baumanii (13.58%). A total of 64 (77.10%) isolates were found to be resistant at least to one of the tested carbapenem antibiotics. Klebsiella pneumoniae (92.30%) and Escherichia coli (75%) were the most represented species.

Table 3 Distribution of Isolates According to the Culture Media

The resistance percentage of the isolates from ESBL agar is presented in Table 4. Notably, high rates of resistance were observed for ofloxacin (ranging from 97.11% to 100%), amoxicillin + clavulanic acid (ranging from 79.41% to 100%), and cefotaxime (ranging from 50% to 100%) among the tested antibiotics. Most Enterobacter cloacae, Escherichia coli, Klebsiella pneumoniae strains demonstrated multidrug resistance. On the other hand, Pseudomonas aeruginosa strains exhibited susceptibility to most of the tested antibiotics, except for cefsulodin. Furthermore, the lowest levels of resistance were observed with antibiotics such as imipenem, ertapenem, and temocillin (Table 4).

Table 4 Percentage of Antibiotic Resistance of Isolates Recovered from ESBL Screening Agar

The study of the resistance profile of Enterobacterales isolates from carba agar was presented in Table 5. The results showed that all isolates (100%) were resistant to amoxicillin and piperacillin. Similarly, a higher rate of resistance was observed to carbapenem, ranging from 87.50% to 100%. Various resistance rates were observed for other antibiotics.

Table 5 Percentage of Antibiotic Resistance of Enterobacterales Isolates Recovered from Carba Screening Agar

The study of the resistance profile of Pseudomonas spp was presented in Table 6. The results showed that isolate of Pseudomonas putida was resistant to all tested antibiotics excepted amikacin. Similarly, 80% (4/5) of Pseudomonas aeruginosa isolates exhibited resistance toward all tested antibiotics but some isolates (3/5) were susceptible to amikacin and aztreonam.

Table 6 Percentage of Antibiotic Resistance of Pseudomonas Spp Isolates Recovered from Carba Screening Agar

The study of the antibiotic resistance profile of Acinetobacter spp was presented in Table 7. The results showed that isolate of Acinetobacter nosocomialis was resistant to all tested antibiotics. Higher resistance rate (>80%) was also observed for Acinetobacter baumannii but 41.66% were susceptible to Amikacin.

Table 7 Percentage of Antibiotic Resistance of Acinetobacter Spp Isolates Recovered from Carba Screening Agar

Distribution of Carbapenemase Types

A total of 64 isolates resistant at least to one of the tested carbapenem antibiotics were selected for NG-TEST CARBA 5 (KPC, OXA-48-like, VIM, IMP, and NDM). All isolates selected were found to be carbapenemase producers. NDM (43.08%) was the most detected type of carbapenemase followed by strains for which carbapenemase type was not detected (30.77%) (Table 8).

Table 8 Distribution of Carbapenemase Types Among Species Isolated by Phenotypic Test

Table 9 Distribution of Detected Carbapenemase Gene Among Species Isolated

Antibiotic Resistance Genes Detection

Tables 9 and 10 present the distribution of resistance genes in carbapenemase and ESBL producing isolates. bla_NDM (54.68%) was the most detected carbapenemase genes. Among the ESBL-producing isolates, the presence of five ESBL genes was identified and presented in Table 10. The most detected genes were bla_CTXM-1 and bla_CTXM-15, accounting for 91.49% of the isolates. Additionally, the bla_TEM (36.17%) and bla_OXA-1 (29.78%) were found in the isolates. Quinolone resistance genes were prevalent among the isolates, with qnrb present in 70.21% of the isolates, followed by qnrs in 63.82% of the isolates, and aac(6′)-ib-cr in 53.19% of the isolates. Among the different species, Escherichia coli exhibited a high prevalence (97.05%) of the bla_CTXM-1 gene, while Enterobacter cloacae had a prevalence of 80% for the same gene. Electrophoresis gel images are shown in supplementary data (Figures S1–S6).

Table 10 Distribution of Resistance Genes in ESBL Producing Isolates

Discussion

The emergence and global spread of extended-spectrum β-lactamase (ESBL) and carbapenemase producing Enterobacterales pose a significant threat to public health.²⁰ The objective of this study was to determine the one-day prevalence of faecal carriage of ESBL and carbapenemase producing Gram-negative bacilli, along with associated risk factors, and to characterize the resistant isolates.

In this study, a culture-dependent approach was used to detect ESBL-producing- and carbapenem-resistant Gram-negative bacteria. The results of this study revealed a significant prevalence of carriage of ESBL-producing bacteria (38.33%), indicating a potential risk for nosocomial infections and the dissemination of antimicrobial resistance genes in the community. Lower rates of ESBL-PE carriage prevalence were reported in other African countries such as Gabon (11.8% to 16.7%), Cameroon (15% to 18%), Central African Republic (19.3%), and Nigeria (20.9%).^41–44 In Madagascar, the prevalence of ESBL-PE among individuals was found to be 10.1%.⁴⁵ On the other hand, a higher prevalence of ESBL-PE was reported in Burkina Faso.⁴⁶ Disparities in prevalence rates among countries and the increasing prevalence of ESBL-producing bacteria in Africa have been highlighted.⁴⁷ However, our findings do not align with their estimation that the overall rate of ESBL-PE in clinical samples was below 15%. Concerning carbapenem resistance detection, out of the 120 samples that were collected, a total of 81 strains were isolated. As 59 were identified as carriers of carbapenem-resistant bacteria, the prevalence of carbapenem resistance carriage is therefore 49.16%. A study conducted in Egypt revealed that 62.7% of Enterobacterales isolates were resistant to carbapenems, which is consistent with our findings.48 Similarly, high rates of carbapenem resistance were observed in South Africa (68%),⁴⁹ and Sudan (83%).⁵⁰ Respectively, 28.6% and 35% rates of resistance were observed in Uganda and Tanzania.^51,52 The prevalence of ESBL-PE values among individuals in different African countries varies significantly not only between countries, as underlined by the authors, but also between cities, sites (such as rural versus urban areas, and hospitals versus communities), and over different years. This variability should be highlighted even if the provided data remain indicative. The unrestricted use of antibiotics, which is prevalent among most of the population in low- and middle-income countries (LMICs) in Africa especially in Benin, is likely to lead to an increase in carbapenem resistance in the region.⁵³ In this study, the samples were collected within one day (24 hours), and it is not possible to estimate whether these results show an intermittent or regular situation in the study area. Sampling at different times of the year could give a more reliable profile of the situation.

In this study, no significant association was found between these risk factors and carriage of ESBL-producing bacteria. Similar investigations focusing on carbapenemase-producing isolates did not yield comparable results.^54,55 The study of the association of variables shows a high degree of association (p < 0.05) for the factors sex, independent walking, and use of a wheelchair. Patients who use wheelchairs or have limited mobility spend generally more time in healthcare facilities, potentially increasing their exposure to resistant isolates. Also, young patient age has been regarded as a risk factor for Carbapenem-resistant Enterobacterales (CRE) infection.⁵⁰ Length of hospital stay, sex, age, presence of immunosuppression, independent walking, bedridden patient, diabetic patient, presence of sign of infection, antibiotic treatment, history of hospitalization in the last 6 months, antibiotic treatment last 6 months, patient with faecal/urinary incontinence were the other risk factors examined in this survey. Neither of these risk factors was significantly associated with carbapenemase producing strains carriage. Numerous studies have shown interest in assessing the risk factors associated to carbapenemase producing isolates carriage but none of them have found similar results.^54,55

In total, 51.08% of the isolated strains were found to be ESBL producers. The identification of ESBL producing bacteria species revealed a predominance of Escherichia coli (82.93%), followed by Enterobacter cloacae (100%; n = 5) and Klebsiella pneumoniae (80%; n = 4). Notably, Enterobacter cloacae, Escherichia coli, and Klebsiella pneumoniae strains exhibited high proportions (100%, 82.93%, and 80% respectively) of ESBL production. Similar species predominance was observed in recent studies.^44,56 Other studies conducted in Cameroon and China have reported a higher diversity of ESBL-PE species, including Enterobacter spp. and Citrobacter spp.^57,58 Nevertheless, E. coli consistently emerges as the most identified species during colonization by ESBL-PE. It is worth noting that these bacteria, which pass through the human intestine, have the potential to acquire resistance through horizontal gene transfer.⁴⁶ The identification of bacteria with resistance to carbapenems showed a predominance of the species Escherichia coli (44.45%) followed by the species Klebsiella pneumoniae (16.06%) and Acinetobacter baumanii (13.58%). Same isolates were reported in various studies.^48–52 Patients with carbapenem-resistant Enterobacterales often face treatment failure, prolonged hospital stay, high expenses and a high possibility of death.⁵⁹

The antibiotic resistance phenotype results of isolates from ESBL agar showed high rate of resistance for ofloxacin (ranging from 97.11% to 100%), amoxicillin + clavulanic acid (ranging from 79.41% to 100%), and cefotaxime (ranging from 50% to 100%) among the tested antibiotics. Multidrug resistance was common among Enterobacter cloacae, Escherichia coli and Klebsiella pneumoniae strains. Similar observations were made,⁴⁴ indicating significant resistance not only to β-lactam antibiotics due to ESBL production but also to other antibiotic families, including quinolones. In this study, the carbapenems (imipenem and ertapenem) showed the highest effectiveness against ESBL-producing Gram-negative bacteria (GNBs). These carbapenems are considered expensive last-resort drugs in the region but have demonstrated favorable activity against GNBs that produce this enzyme.^60–62 This finding is consistent with a report which indicates the widespread distribution of ESBL producers among Enterobacterales.⁵⁰ The study of antibiotic susceptibility patterns of carbapenem-resistant isolates revealed that the majority of isolates were resistant to all tested antibiotics, including carbapenems. Distribution of carba types according to species isolated and ESBL production showed that 52.31% of isolates were ESBL producers, but none non-Enterobacterales was found to be ESBL producing. All isolates selected were found to be carbapenemase producers. NDM (43.08%) was the most detected type of carbapenemase followed by other types (30.77%) using the aforementioned phenotypic method. The NDM enzyme was frequently encountered among Enterobacterales as one of the most common carbapenemases, and it was also observed in A. baumannii and P. aeruginosa isolates.^63,64 Most of non-Enterobacterales were found to produce other types of carbapenemase. This is consistent with a study reporting that carbapenemase producers are becoming highly distributed among Enterobacterales.⁵⁰ Non-Enterobacterales including Pseudomonas spp employ mainly porin expression reduction and increased chromosomal cephalosporinase activity against carbapenems.⁶⁵

The study extensively examined the presence of ESBL genes among the isolates. The most frequently detected ESBL genes were bla_CTXM-1 and bla_CTXM-15 (91.49%), followed by bla_TEM (36.17%). These genes are associated with resistance to a broad range of β-lactam antibiotics. Similar findings have been reported in other studies conducted in Cameroon (96% of isolates), Indonesia (94.5%), and Tunisia (91%), indicating the global dominance of CTX-M-type ESBLs.^57,66,67 The study also investigated the presence of other resistance genes associated with quinolone and carbapenem resistance. The isolates exhibited the presence of quinolone resistance genes, such as qnrb (70.21%), qnrs (63.82%), and aac(6′)-ib-cr (53.19%), indicating the potential for reduced efficacy of fluoroquinolone antibiotics in treating infections caused by ESBL-producing bacteria. This result supports the concept co-expression due to the isolates harboring several antibiotic resistance genes. Furthermore, the presence of qnr genes and aac(6′)-ib-cr alongside ESBL-encoding genes further strengthens the possibility of co-selection.⁶⁸ These findings confirm the multidrug-resistant nature of ESBL-PE, which severely limits available therapeutic options and contributes to the widespread use of carbapenems. The detection of carbapenemase-encoding genes in a study is vital for understanding and addressing the growing problem of antibiotic resistance. It provides critical information for infection control, treatment decisions, and the development of strategies to preserve the effectiveness of antibiotics in the face of rising resistance. In this study, bla_NDM (54.68%) was the most detected carbapenemase genes mainly in Klebsiella pneumoniae (91.66%), Enterobacter cloacae (77.77%) and Pseudomonas aeruginosa (75%). Our results are consistent with others studies in Sudan, Senegal, and South Africa.^49,50,59 This confirms that bla_NDM could demonstrate a capacity for rapid dissemination.⁶⁹ The plasmids that carry carbapenemase genes, such as bla_NDM, exhibit significant diversity and can harbor numerous additional resistance genes, including ESBL-alleles. The presence of multiple resistance genes in certain isolates, as observed in this study, indicates the presence of multidrug-resistant pathogens. These pathogens are responsible for treatment failures, outbreaks of infections, and increased treatment costs.

The primary aim was to determine the prevalence of ESBL and carbapenemase-producing bacteria at a single time point in hospitalized post-surgical patients, providing baseline data for future longitudinal studies on acquisition dynamics. The one-day design and >48-hour hospitalization criterion limit differentiation between community- and hospital-acquired colonization. Without admission screening or follow-up, acquisition timing and transmission pathways cannot be confirmed. Also, Limited clinical and hospital data restrict analysis of risk factors and transmission routes. Therefore, conclusions about nosocomial spread should be interpreted cautiously. The absence of whole genome sequencing prevents detailed epidemiological and transmission analysis. Future studies with longitudinal sampling, comprehensive data, and genomic tools are needed to clarify acquisition and guide interventions.

Conclusion

This study reports a high one-day prevalence of fecal carriage of extended-spectrum β-lactamase (ESBL) and carbapenemase-producing Gram-negative bacteria among post-surgical patients hospitalized for over 48 hours in Benin. The predominance of Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Acinetobacter baumannii, combined with high resistance to β-lactams and fluoroquinolones, underscores the clinical and public health significance of these pathogens. Genotypic analysis revealed frequent detection of bla_CTX-M and bla_NDM, along with co-resistance genes, indicating the circulation of multidrug-resistant organisms in the hospital setting. While this cross-sectional study cannot confirm nosocomial acquisition, it provides important baseline data to inform future surveillance and infection control efforts in Beninese hospitals.

Abbreviations

CTX-M: Cefotaxime-Munich; ESBL: Extended-Spectrum β-lactamase; ESBL-PE: ESBL Producing Enterobacterales; GNB: Gram Negative Bacilli; MBL: Metallo-betalactamase; NDM: New Delhi Metallo-betalactamase.

Data Sharing Statement

All data generated and/or analyzed during the current study are included in this published article. The datasets used and/or analyzed during this study are also available from the corresponding author on reasonable request.

Ethical Approval

The study proposal was reviewed and approved by the Ethics and Research Committee of the Institute of Applied Biomedical Sciences (CER-ISBA) under number 154. Written informed consent was obtained from each patient or their parent/guardian before participation, accompanied by a concise explanation of the study’s objective. The research work (sampling from hospitalized patients, sample processing and data analysis) in our study was conducted in accordance with the Declaration of Helsinki.

Acknowledgments

The authors are very grateful to Alida Oussou, Donald Bokossa, Lydie Comlan, Adonias Houefonde and Yedia Djohoun for their great help during the implementation of this study. They are also very grateful to all the staff of the hospitals involved in the study and their willing to support any kind of interventions for a better care of patients. They thank the patients who accepted to participate in this study.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This research received no external funding.

Disclosure

The authors declare no conflict of interest.

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Introduction

Obstructive sleep apnea (OSA) is characterized by recurrent sleep disturbances leading to intermittent hypoxemia, hypercapnia, and disrupted sleep architecture.^1–3 An epidemiological study indicates that the estimated prevalence of OSA with an apnea-hypopnea index (AHI) ≥5 events/hour ranges from 17% to 26% in men and 9% to 28% in women, with rates of 4% to 9% in middle-aged men and 1% to 2% in middle-aged women.⁴ According to the diagnostic criteria of AHI ≥5 events/hour, it is estimated that there are 176 million OSA patients in China, with 66 million being moderate to severe cases.^5,6 Studies have demonstrated that OSA can lead to nocturnal hypoxia, resulting in sympathetic activation, inflammation, oxidative stress, metabolic disorders, and endothelial dysfunction, potentially leading to multi-organ and multi-system damage such as cardiovascular diseases, diabetes, arrhythmias, cerebrovascular accidents, and cognitive impairments.^3,7–9 Untreated severe OSA patients have a mortality rate 3.8 times higher than that of the general population. The probability of stroke occurrence in the OSA population is 4.33 times higher than in the control group, with a mortality rate 1.98 times higher than the control group.^10,11

The primary assessment indicators for OSA currently include total AHI, lowest blood oxygen saturation, longest apnea duration, and longest hypoventilation period. Clinical experience demonstrates that relying solely on these indicators does not provide a comprehensive reflection of the severity of the patient’s condition. The longest apnea duration, longest hypoventilation period, and lowest blood oxygen saturation measurements offer insights at specific time points, while the AHI value merely indicates the frequency of events throughout the night without capturing the duration and extent of hypoxia.^12,13 This lack of effective evaluation indicators leads to inadequate understanding and assessment of OSA conditions, resulting in insufficient patient awareness of the condition’s severity and subsequently lower rates of consultation, diagnosis, and treatment for OSA.¹⁴ Economical and efficient screening for patients with OSA, coupled with a thorough evaluation of their condition, can enhance patient awareness and facilitate early intervention, thereby generating significant societal benefits and enhancing the overall quality of life for the population. The China-PAR model has been developed to validate 10-year risk predictions for atherosclerotic cardiovascular disease (ASCVD) among the Chinese population. This model incorporates various factors, including gender, age, waist circumference, total cholesterol, high-density lipoprotein cholesterol, treated or untreated systolic blood pressure, current smoking status (yes/no), diabetes mellitus (yes/no), and family history of ASCVD.¹⁵ It emphasizes anthropometric measurements and risk factors that are commonly observed in patients with OSA. Therefore, the China-PAR model is particularly suitable for assessing the 10-year ASCVD risk in patients with OSA in our study.

The investigation into cardiovascular risks among patients with OSA during episodes of hypoxia is gaining increasing significance. Nocturnal hypoxia can induce oxidative stress, inflammation, sympathetic activation, endothelial dysfunction, metabolic dysregulation, and pro-thrombotic effects and drive ASCVD and myocardial ischemia.^16–18 Previous studies have shown that recurrent hypoxia in OSA is related to pulmonary arterial hypertension, and vascular and microvascular diseases in patients.^1,2,10,11,19 The importance of new body indices and hypoxic burden metrics in the assessment of cardiovascular disease risk in OSA patients cannot be overlooked. For example, Yeşildağ M et al reported that the body roundness index, a novel body metric for defining and predicting cardiovascular risk in patients with OSA, was significantly associated with the oxygen desaturation index, a critical metric that measures hypoxic burden and links OSA to cardiovascular conditions.²⁰ However, the association between hypoxia events and patient prognosis assessment remains unclear. The hypoxic burden is calculated by dividing the sum of the product of the desaturation area associated with each pause or hypopnea event by the total sleep time. It can be automatically calculated by advanced sleep monitoring data analysis software, reflecting the intensity and duration of hypoxic events.²¹ The percentage of time with blood oxygen saturation below 90% (T90%) reflects the total duration of hypoxia throughout the night.²² However, due to the lack of comprehensive prognostic analysis, there are currently no standardized grading criteria for the hypoxic burden and T90%. The role of hypoxic burden and T90% in assessing the condition and prognosis of OSA patients remains to be further explored.

Polysomnography (PSG) is considered the primary diagnostic tool for identifying OSA. However, the equipment associated with PSG is costly, requires a specific installation environment, involves complex operation, demands high proficiency from medical personnel, necessitates completion in medical facilities during the examination process, and often leads to a suboptimal patient experience. These factors make it challenging to use PSG efficiently in screening for OSA among high-risk populations. Studies have shown that portable sleep monitors demonstrate a high level of agreement with PSG outcomes and offer reliable diagnostic capabilities for OSA, prompting an increased recognition of their clinical utility.^23,24

Here, by using portable sleep apnea monitors, we investigated the relationship between oxygen desaturation burden and T90% and myocardial ischemia and cardiovascular risk, promoting the establishment of a more comprehensive OSA assessment system. Our research has the potential to serve as a foundation for OSA patients to receive enhanced diagnostic and therapeutic interventions, ultimately leading to improved patient outcomes.

Materials and Methods

Ethics Statement

This study was carried out in accordance with the Declaration of Helsinki. All procedures were performed in compliance with relevant laws and institutional guidelines and have been approved by the Ethics Committee of Shenzhen Qian Hai She Kou Free Trade Zone Hospital (No.: 2021K-057). Informed content was obtained from all patients.

Study Participants

This prospective observational study was conducted at a single tertiary center in Southern China from January 2022 to March 2024. Hospitalized patients who underwent portable sleep apnea screening tests after being highly suspected of having OSA were screened. Only patients who were subsequently diagnosed with OSA were included. Inclusion criteria: 1) patients with daytime sleepiness, lack of energy after waking up, fatigue, or insomnia. 2) patients who woke up due to suffocation, wheezing, or choking at night. 3) patients with habitual snoring or breathing interruptions. 4) patients with dizziness. 5) patients with hypertension or poor response to hypertension treatment. 6) patients with emotional disorders. Exclusion criteria: 1) patients with known coronary artery diseases; 2) patients with a history of stroke; 3) patients with arrhythmias or those who used antiarrhythmic medications; 4) patients with cardiomyopathy; 5) patients with valvular heart disease; 6) patients who had electrolyte imbalances; 7) patients with severe kidney insufficiency.

Diagnostic Criteria for OSA

The diagnosis and initial assessment of OSA were based on the International Classification of Sleep Disorders.²⁵ The diagnostic threshold for OSA was defined by an AHI of ≥ 5 events per hour, where AHI values of 5–15 were classified as mild, 15–30 as moderate, ≥ 30 as severe, and ≥ 60 as extremely severe. The severity of nocturnal hypoxia was classified into five categories: normal, mild, moderate, severe, and very severe, determined by the lowest oxygen saturation levels: ≥90%, 85%-89%, 80%-84%, and <80%.

Sleep Monitoring

Participants were instructed to wear the portable sleep monitor, Phillip Alice Night One (Respironics, CA, USA), for overnight sleep monitoring. This device has been validated against PSG and has demonstrated a high level of agreement with PSG outcomes.^23,24 Sleep duration was assessed by considering the position, nasal airflow patterns, and self-reported sleep onset time of patients. Data was collected using Sleepware G3 4.0.1.0 (Respironics, CA, USA), manually reviewed and interpreted. Then, the hypoxic burden and other assessment parameters were automatically calculated. The recorded indicators included: AHI, hypoxic burden, supine hypoxic burden, non-supine hypoxic burden, lowest blood oxygen saturation, percentage of total sleep time with apneas, percentage of total sleep time with hypopneas, and percentage of total sleep time with blood oxygen saturation below 90% (T90%), 85% (T85%), 80% (T80%).

Electrocardiogram (ECG) Examinations

All patients underwent routine ECG examinations. The ECGs were assessed and reported by physicians at or above the attending level. The ECG indicators for evaluating myocardial ischemia included: 1) ST segment depression or elevation (ST segment depression in two or more adjacent leads, with limb leads ≥ 0.05 mV or precordial leads ≥ 0.1 mV); 2) T wave flattening or inversion); 3) ST-T changes; 4) positive PTFV1 (P-wave terminal force in electrocardiogram lead V1).

Assessment of the 10-Year Atherosclerotic Cardiovascular Disease (ASCVD) Risk

The process for assessing the 10-year ASCVD risk using the China-PAR model involved two steps.¹⁵ In Step 1, high-risk individuals were directly identified based on specific criteria, including 1) diabetic patients aged ≥40 years; 2) LDL ≥4.9 mmol/L or total cholesterol ≥7.2 mmol/L; 3) chronic kidney disease at stage 3/4. Due to the potential impact of kidney insufficiency on electrolytes and electrocardiographic waveforms, patients with chronic kidney insufficiency were excluded from this study. In Step 2, the remaining patients were subjected to the 10-year ASCVD risk assessment using the China-PAR model. Patients were categorized as either having low or moderate-to-high risk of ASCVD.

Data Collection

The baseline clinical data of patients were collected, including medical history, personal history, medication history, height, weight, abdominal circumference, and current residence. The data on biochemical indicators (such as total cholesterol, high-density lipoprotein, low-density lipoprotein (LDL), and triglycerides) were also collected.

Sample Size Calculation and Power Analysis

The sample size was determined using the standard formula. Assuming a moderate effect size corresponding to an expected odds ratio (OR) of approximately 3–10 and an estimated risk of ASCVD around 50% (α=0.05), we calculated a necessary sample size of approximately 200 to 350 participants for adequate statistical power.

Power analysis was conducted to validate the statistical power of our sample size, using the following formula: Power = 1 – β, where β indicates Type II error rate (commonly set at 0.2 for 80% power).

Statistical Analysis

Statistical analyses were conducted using SPSS 19.0 software. The normally distributed continuous data are expressed as mean ± standard deviation, and comparisons between two groups were performed using independent samples t-test. The non-normally distributed continuous data are expressed as median (interquartile range), and group comparisons were conducted using the Kruskal–Wallis H-test. Categorical data are presented as counts (percentages) and were analyzed using the χ²-test. A P-value of < 0.05 was considered statistically significant.

Binary logistic regression was used to analyze the relationships between the factors in the model and the risk for moderate-to-high 10-year ASCVD and myocardial ischemia. The factors analyzed in the model included AHI, hypoxic burden quartiles, percentage of total sleep time with apneas, percentage of total sleep time with hypopneas, percentage of total sleep time with apneas and hypopneas, T90%, T85%, T80%, and grade of lowest blood oxygen saturation. Hypoxic burden, percentage of total sleep time with hypopneas, percentage of total sleep time with apneas, and percentage of total sleep time with apneas and hypopneas were categorized into four quartiles. Confounders including gender, age, waist circumstance, total cholesterol, high-density lipoprotein, treated or untreated systolic blood pressure, current smoking (yes/no), diabetes mellitus (yes/no), and family history of ASCVD were adjusted. The OR and 95% confidence interval (CI) values were recorded. The receiver operating characteristic (ROC) curve was plotted to assess the diagnostic values of the hypoxic burden and T90%. The area under the curve (AUC), P-value, the optimal cut-off value, sensitivity, specificity, and Youden’s index were calculated. The maximum Youden’s index corresponds to the optimal balance of sensitivity and specificity, thereby determining the optimal cutoff value.

Results

Baseline Clinical Data of Patients

This study included a total of 311 hospitalized patients diagnosed with OSA from January 2022 to March 2024. Basic clinical information of the patients is summarized in Table 1. The median age of the included patients was 53 years, with 75.6% being male and 24.4% female. Among the patients, 51.4% (160 cases) showed ECG changes of myocardial ischemia. Additionally, 55.3% (172 cases) had moderate-to-high 10-year ASCVD risk. As shown in Table 2, the first to third hypoxic burden quartiles for patients with low 10-year ASCVD risk were 44.2 min/h, 73.8 min/h, and 123.4 min/h, respectively. In contrast, the corresponding quartiles for patients with moderate to high risk were 83.0 min/h, 176.1 min/h, and 373.2 min/h. For patients who exhibited negative changes on the ECG indicating myocardial ischemia, the first to third quartiles were 42.8 min/h, 69.8 min/h, and 129.4 min/h, compared to 95.3 min/h, 179.3 min/h, and 384.3 min/h for those with positive changes.

Table 1 Clinical Information of Patients

Table 2 The Quartile Distribution of Hypoxic Burden Among Patients with Moderate-to-High ASCVD Risk and Myocardial Ischemia

Comparison of Sleep Monitoring Characteristics in Patients with Different ASCVD Risk Levels

Kruskal–Wallis H-test indicated significant differences in sex, age, and medical history (Table 3). Independent samples t-test revealed no significant difference between the low 10-year ASCVD risk group and the moderate-to-high 10-year ASCVD risk group with the lowest blood oxygen saturation (Table 3). However, significant differences were observed in AHI, hypoxic burden, supine hypoxic burden, non-supine hypoxic burden, percentage of total sleep time with apnea, percentage of total sleep time with hypopnea, percentage of total sleep time with apnea and hypopnea, T90%, T85%, and T80% (Table 3).

Table 3 Comparison of Sleep Monitoring Parameters in OSA Patients with Low versus Moderate-to-High 10-year ASCVD Risk

Analysis of Baseline Clinical Data and Sleep Monitoring Measures Concerning Myocardial Ischemia Changes

As shown in Table 4, the Kruskal–Wallis H-test indicated that there were no significant differences in sex and age between patients with and without myocardial ischemia changes on ECG. The independent samples t-test showed no significant differences in body mass index, total cholesterol, high-density lipoprotein, or LDL between the two groups. In terms of sleep monitoring indicators, there were no significant differences between the two groups in the percentage of total sleep time with hypopneas, T85%, T80%, or lowest blood oxygen saturation. However, significant differences were found in AHI, hypoxic burden, supine hypoxic burden, non-supine hypoxic burden, percentage of total sleep time with apneas, percentage of total sleep time with apneas and hypopneas, and T90% between the two groups.

Table 4 Comparison of Clinical Data and Sleep Monitoring Indicators Between Patients with and without Myocardial Ischemia Changes on ECG

Analysis of Factors Influencing 10-Year ASCVD Risk and Myocardial Ischemia in OSA Patients

The binary logistic regression analysis was then performed to evaluate factors influencing 10-year ASCVD risk and myocardial ischemia in OSA patients. The factors included in the model were hypoxic burden grading, AHI grading, percentage of total sleep time with hypopnea, percentage of total sleep time with apnea, percentage of total sleep time with apnea and hypopnea, T90%, T85%, and T80%. Our results showed that hypoxic burden and T90% were significantly associated with the high 10-year ASCVD risk in OSA patients. For the index of hypoxic burden grade, the P-value was 0.024, while hypoxic burden between 75%-100% quartile showed statistically significant risk compared with patients in 0%-25% quartile (Q1), with an OR of 13.5 and a 95% CI of 1.684 to 109.051 (Table 5). For T90%, the P-value was 0.022, with an OR of 1.085 and a 95% CI of 1.012 to 1.164 (Table 5). Similarly, hypoxic burden grade and T90% were significantly associated with myocardial ischemia in patients with OSA. For the index of hypoxic burden grade, the P-value was 0.044. Both Q2, Q3, and Q4 exhibited statistically significant risks compared to patients in the 0%-25% quartile (Q1), with P-values of 0.011, 0.008, and 0.048, respectively. The OR was 3.241 (95% CI: 1.306–8.043), 4.497 (95% CI: 1.494–13.538), and 4.850 (95% CI: 1.017–23.139) for Q2, Q3, and Q4, respectively. For T90%, the P-value was 0.019, with an OR of 1.059 and a 95% CI of 1.009 to 1.110 (Table 6). Additionally, the power analysis showed that with the expected ORs of the hypoxic burden ranging from 3 to 11, our study provided a power greater than 90%. For T90%, even though the ORs were between 1.059 and 1.085, as a continuous variable, the analysis indicated that changes in this index across its range still resulted in sufficient power, estimated between 85% and 92%, confirming the reliability of our findings. These results revealed a significant association between T90% and hypoxic burden with high 10-year ASCVD risk and myocardial ischemia.

Table 5 Analysis of Risk Factors for High 10-Year ASCVD Risk in OSA Patients

Table 6 Analysis of Risk Factors for Myocardial Ischemia in OSA Patients

ROC Curve analysis of the Diagnostic Value of Hypoxic Burden for Moderate-to-High 10-Year ASCVD Risk and Myocardial Ischemia in OSA Patients

To determine the value of the hypoxic burden and T90% in diagnosing 10-year ASCVD risk and myocardial ischemia, the ROC curve was plotted. The results showed that the hypoxic burden and T90% (Figure 1) had diagnostic values for moderate-to-high 10-year ASCVD risk (Table 7). For hypoxic burden, the area under the curve was 0.747, with P<0.0001. The Youden’s index was used to determine the cut-off value of the hypoxic burden, which was 125.8 (%min/h). The corresponding sensitivity and specificity were 62.2% and 77.0%, respectively. For T90%, the AUC was 0.754. The cut-off value of T90% was 3.05%, with the corresponding sensitivity and specificity of 65.1% and 71.9%, respectively.

Table 7 Diagnostic Value of Hypoxic Burden and T90% for Moderate-to-High 10-Year ASCVD Risk in OSA Patients

Figure 1 ROC curve analysis of hypoxic burden and T90% in diagnosing moderate-to-high 10-year ASCVD risk in OSA patients.

Similarly, the hypoxic burden and T90% (Figure 2) also showed diagnostic values for myocardial ischemia (Table 8) in OSA patients. The AUC for the hypoxic burden and T90% was 0.769 and 0.741, respectively. The cut-off value for the hypoxic burden was 112.6 (%min/h), with a sensitivity of 70.6% and a specificity of 70.2%. For T90%, the cut-off value was 4.20%, the sensitivity was 60.6%, and the specificity was 77.5%.

Table 8 Diagnostic Value of Hypoxic Burden and T90% for Myocardial Ischemia in OSA Patients

Figure 2 ROC curve analysis of hypoxic burden and T90% in diagnosing myocardial ischemia in OSA patients.

Discussion

The results of this study revealed a strong association between hypoxic burden and T90% and both moderate-to-high 10-year ASCVD risk and myocardial ischemia in OSA patients. These indicators, reflecting the intensity and duration of hypoxic events during sleep, provided valuable insights into the severity of OSA and its cardiovascular implications. In fact, the cutoff values derived from this study are highly consistent with clinically observed OSA severity and have significant implications for evaluating patients with a high AHI, short-duration events, and low overall nocturnal hypoxia. These metrics provide essential clinical guidance in determining whether patients require more aggressive treatments, such as continuous positive airway pressure therapy. Rather than relying solely on AHI and minimum oxygen saturation for severity assessment, clinicians are more inclined to consider measures of hypoxia duration and intensity. The hypoxic burden index and T90% more accurately meet the requirements of clinicians. Notably, the traditional evaluation indicators, such as AHI and the lowest blood oxygen saturation, did not have such associations, highlighting the limitations of current OSA severity assessment indicators. The higher hypoxic burden and T90% underscore the risk of intensity and duration of hypoxic events in 10-year ASCVD risk and myocardial ischemia for OSA patients. Additionally, the findings emphasize the importance of incorporating these advanced indicators into routine clinical practice for a more comprehensive evaluation of cardiovascular risk in OSA patients. These indicators can help clinicians identify high-risk patients early and develop tailored treatment strategies.

The association of cardiovascular risk with hypoxic events in OSA patients is receiving increasing attention. Previous studies^1,2,10,11,19 have indicated that recurrent hypoxia in OSA is related to pulmonary hypertension and vascular and microvascular diseases. The oxygen desaturation index, a critical metric that measures hypoxic burden, links OSA to cardiovascular conditions in the context of hypoxia. It is correlated with the body roundness index, a novel body metric used to define and predict cardiovascular risk in patients with OSA.²⁰ However, the relationship between hypoxic events and patient prognosis still requires further investigation. The hypoxic burden, which reflects the intensity and duration of hypoxic events, can be automatically calculated by advanced sleep monitoring data analysis software. The T90% reflects the total time spent with blood oxygen saturation below 90% throughout the night, providing insight into the overall severity of OSA during the night.²² This study indicates that the hypoxic burden and T90% were significantly related to both the 10-year ASCVD risk and myocardial ischemia. As the increase of the hypoxic burden, the risk of moderate-to-high 10-year ASCVD and myocardial ischemia also elevated. Although previous studies have investigated the relationship between T90% and hypoxic burden-related indicators and cardiovascular disease, there is currently a lack of cut-off values for these indicators for risk prediction, leading to an inability to classify the severity of patients and resulting in different research conclusions.^26,27 This study established cut-off values for T90% and hypoxic burden in predicting the risk of myocardial ischemia and cardiovascular disease. These results may provide a basis for defining the severity and offer important guidance for the clinical treatment of myocardial ischemia and cardiovascular complications in patients with OSA.

PSG is limited in clinical use due to its high cost and complex operation. Portable sleep monitors, however, show high consistency with PSG in diagnosing OSA.²³ This study utilized portable sleep monitoring and included 311 OSA patients of different ages and genders from southern China. Given that the portable sleep monitor we used in this study is validated for accuracy and reliability against traditional PSG, it enables clinicians to perform comprehensive assessments in a variety of settings, including home care. This flexibility allows for the timely identification of at-risk patients and could facilitate earlier interventions, which are critical for aggressive management strategies in OSA. Furthermore, the China-PAR model, which is tailored for the Chinese population, was used to assess the 10-year ASCVD risk, allowing for an evaluation of long-term cardiovascular risk. Additionally, ECG was used to assess current myocardial ischemia, providing a comprehensive assessment of both long-term cardiovascular risk and current myocardial ischemia risk in OSA patients.

This study has some limitations. For example, the exclusion of patients with electrolyte disturbances and kidney insufficiency might have underestimated the impact of oxygen desaturation on myocardial ischemia and cardiovascular risk assessment. Another limitation of this study is the modest AUC values and small ORs of the hypoxic burden and T90%, which may limit their clinical utility. Further research is warranted to confirm our findings and explore the applicability of these indices in diverse clinical settings.

Conclusion

This study demonstrates that hypoxic burden and T90% are valuable indicators for assessing cardiovascular risk and myocardial ischemia in OSA patients. These metrics have diagnostic potential for identifying myocardial ischemia and cardiovascular diseases, with implications for clinical practice. While these findings highlight the diagnostic potential of nocturnal hypoxia monitoring for cardiovascular complications, our study is single-center research; further validation in larger, prospective cohorts is necessary before widespread clinical adoption. Additionally, the impact of hypoxia on both short-term and long-term cardiovascular risk events in OSA patients warrants further research and attention.

Abbreviations

OSA, Obstructive sleep apnea; ASCVD, Atherosclerotic cardiovascular disease; PSG, Polysomnography; ECG, Electrocardiogram.

Data Sharing Statement

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Author Contributions

Wenmei Zeng: Conceptualization, Methodology, Resources, Software, Funding acquisition, Writing – original draft, Writing – review & editing. Sulong Wu: Conceptualization, Methodology, Resources, Writing – original draft, Writing – review & editing. Zhuofan Liu: Conceptualization, Investigation, Resources, Writing – review & editing. Long Yuan: Conceptualization, Investigation, Resources, Writing – review & editing. Bilin Chen: Software, Methodology, Resources, Writing – review & editing. Yan Rong: Conceptualization, Funding acquisition, Formal analysis, Data curation, Writing – review & editing, Project administration. All authors gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This work was supported by the Technical R & D and Creative Design Project sub-funds, Nanshan, Shenzhen, China [grant numbers NS2021002; NS2022013].

Disclosure

The authors report no conflicts of interest in this work.

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Your eyes do more than show your brain what’s happening in the world around you. They also reveal how well your brain may fare in the years ahead, including whether or not you might develop dementia.

A long‑running study of 8,623 adults has found that a subtle slowdown in detecting a faint triangle on a computer screen can hint at Alzheimer’s disease up to 12  years before diagnosis.

Lead author Eef Hogervorst of Loughborough University says the simple test “could slot into routine checkups without adding a single drop of blood.”

Vision timing signals brain trouble

Participants pressed a button when they spotted a triangle drifting amid random dots. Those who later developed dementia needed roughly two extra seconds – a gap large enough to raise their future risk by 56 percent. 

The task measures visual processing speed, the time the brain takes to register and respond to a stimulus. Sluggish scores predicted dementia even after researchers adjusted for age, education, and cardiovascular health.

“Visual sensitivity is related to memory performance,” noted Hogervorst. She adds that eyesight often declines quietly, leaving people unaware until memory falters.

A similar pattern appeared in an independent analysis showing that early amyloid plaques disrupt visual signals before memory centers suffer. Taken together, the findings suggest eye‑based tests could extend the warning window for preventive care.

Eyes offer dementia warning

The retina is an outgrowth of the brain, so toxic proteins can accumulate there first. Researchers now examine retinal layers for thinning, abnormal blood vessels, and microscopic deposits that mirror cerebral changes.

Damage often begins in the occipital cortex, the region that deciphers vision, before spreading to the hippocampus. That makes contrast sensitivity, color discrimination, and motion detection early casualties.

People with Alzheimer’s also struggle to ignore distractions. “These problems could increase the risk of driving accidents,” warned Thom Wilcockson, a psychologist at Loughborough University.

Eye-tracking research confirms the concern, showing that older drivers with dementia exhibit erratic saccades, longer fixations, and reduced scanning range – all linked to crash risk.

What slowing detection really means

Spotting a shape on a screen sounds trivial, yet it taps fast neural circuits shared with memory. When those circuits lag, forgetting names and appointments may follow.

The Norfolk data showed the triangle test remained significant after standard memory exams were considered. In clinics, combining both could improve accuracy while saving time and cost.

Slower vision also correlates with trouble recognizing faces, a social cue often missed in early dementia. Patients skim past eyes and mouths, failing to “imprint” new acquaintances and later feeling lost in familiar rooms.

Several groups are testing whether directed eye exercises can sharpen recall. Early trials of rapid left‑right movements report modest gains, though results remain mixed.

Smartphones join dementia fight

High‑grade eye trackers once cost thousands of dollars. A California team has squeezed similar optics into a smartphone app that uses the front‑facing infrared camera to measure pupil changes.

The prototype walks users through a brief pupil dilation task, then uploads data for cloud analysis. Engineers hope the approach will let families monitor brain health between clinic visits.

Consumer wearables are also inching closer. Some virtual‑reality headsets already track gaze direction at millisecond resolution, offering another route for large‑scale screening.

Still, technology alone is not enough. Experts stress the need for clear guidelines to avoid false alarms and protect privacy.

Fight dementia with eye health

Lifestyle counts, too. A 14‑year study of almost 2,000 older adults found those who read at least once a week cut cognitive decline odds by nearly half.

Reading, watching subtitles, or knitting forces the eyes to dart and refocus, a workout for neural networks. Longer education and regular exercise add extra cognitive reserve, buffering the toll of disease.

Optometrists recommend annual exams after people reach 60 years of age.

Reporting new glare, color shifts, or slowed adaptation can nudge physicians to order broader cognitive checks.

Vision‑friendly habits help, too. Good lighting, high‑contrast text, and blue‑green filters reduce strain and may delay functional loss.

Finally, keep blood vessels healthy. Controlling blood pressure, diabetes, and cholesterol supports both retinal and cerebral circulation, tying the two organs together.

The study is published in Scientific Reports.

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